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Bofa A, Zewotir T. Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation. BMC Bioinformatics 2024; 25:168. [PMID: 38678218 PMCID: PMC11056055 DOI: 10.1186/s12859-024-05791-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 04/18/2024] [Indexed: 04/29/2024] Open
Abstract
This study investigates the impact of spatio- temporal correlation using four spatio-temporal models: Spatio-Temporal Poisson Linear Trend Model (SPLTM), Poisson Temporal Model (TMS), Spatio-Temporal Poisson Anova Model (SPAM), and Spatio-Temporal Poisson Separable Model (STSM) concerning food security and nutrition in Africa. Evaluating model goodness of fit using the Watanabe Akaike Information Criterion (WAIC) and assessing bias through root mean square error and mean absolute error values revealed a consistent monotonic pattern. SPLTM consistently demonstrates a propensity for overestimating food security, while TMS exhibits a diverse bias profile, shifting between overestimation and underestimation based on varying correlation settings. SPAM emerges as a beacon of reliability, showcasing minimal bias and WAIC across diverse scenarios, while STSM consistently underestimates food security, particularly in regions marked by low to moderate spatio-temporal correlation. SPAM consistently outperforms other models, making it a top choice for modeling food security and nutrition dynamics in Africa. This research highlights the impact of spatial and temporal correlations on food security and nutrition patterns and provides guidance for model selection and refinement. Researchers are encouraged to meticulously evaluate the biases and goodness of fit characteristics of models, ensuring their alignment with the specific attributes of their data and research goals. This knowledge empowers researchers to select models that offer reliability and consistency, enhancing the applicability of their findings.
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Affiliation(s)
- Adusei Bofa
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu Natal, Oliver Tambo Building, Westville Campus, Durban, South Africa.
| | - Temesgen Zewotir
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu Natal, Oliver Tambo Building, Westville Campus, Durban, South Africa
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2
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Katsaounis D, Harbour N, Williams T, Chaplain MA, Sfakianakis N. A Genuinely Hybrid, Multiscale 3D Cancer Invasion and Metastasis Modelling Framework. Bull Math Biol 2024; 86:64. [PMID: 38664343 PMCID: PMC11045634 DOI: 10.1007/s11538-024-01286-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/22/2024] [Indexed: 04/28/2024]
Abstract
We introduce in this paper substantial enhancements to a previously proposed hybrid multiscale cancer invasion modelling framework to better reflect the biological reality and dynamics of cancer. These model updates contribute to a more accurate representation of cancer dynamics, they provide deeper insights and enhance our predictive capabilities. Key updates include the integration of porous medium-like diffusion for the evolution of Epithelial-like Cancer Cells and other essential cellular constituents of the system, more realistic modelling of Epithelial-Mesenchymal Transition and Mesenchymal-Epithelial Transition models with the inclusion of Transforming Growth Factor beta within the tumour microenvironment, and the introduction of Compound Poisson Process in the Stochastic Differential Equations that describe the migration behaviour of the Mesenchymal-like Cancer Cells. Another innovative feature of the model is its extension into a multi-organ metastatic framework. This framework connects various organs through a circulatory network, enabling the study of how cancer cells spread to secondary sites.
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Affiliation(s)
- Dimitrios Katsaounis
- School of Mathematics and Statistics, University St Andrews, North Haugh, St Andrews, UK.
| | - Nicholas Harbour
- School of Mathematical Sciences, University Nottingham, Nottingham, UK
| | - Thomas Williams
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Mark Aj Chaplain
- School of Mathematics and Statistics, University St Andrews, North Haugh, St Andrews, UK
| | - Nikolaos Sfakianakis
- School of Mathematics and Statistics, University St Andrews, North Haugh, St Andrews, UK
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3
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Bi X, Czajkowsky DM, Shao Z, Ye J. Digital colloid-enhanced Raman spectroscopy by single-molecule counting. Nature 2024; 628:771-775. [PMID: 38632399 DOI: 10.1038/s41586-024-07218-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Daniel M Czajkowsky
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zhifeng Shao
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
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4
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Born DP, Lorentzen J, Björklund G, Stöggl T, Romann M. Variation vs. specialization: the dose-time-effect of technical and physiological variety in the development of elite swimmers. BMC Res Notes 2024; 17:48. [PMID: 38355679 PMCID: PMC10865614 DOI: 10.1186/s13104-024-06706-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 01/26/2024] [Indexed: 02/16/2024] Open
Abstract
OBJECTIVE It is heavily discussed whether larger variety or specialization benefit elite performance at peak age. Therefore, this study aimed to determine technical (number of different swimming strokes) and physiological (number of different race distances) variety required to become an international-class swimmer (> 750 swimming points) based on 1'522'803 race results. RESULTS Correlation analyses showed lower technical variety in higher ranked swimmers (P < 0.001), yet with small effects (0.11-0.30). However, Poisson distribution revealed dose-time-effects and specified number of swimming strokes required during each age group. Specifically, freestyle swimmers showed highest chances when starting to compete in three to four swimming strokes but reduced their variety to three swimming strokes at the ages of 12/13yrs with another transition to two swimming strokes at the ages of 19/21yrs (female/male swimmers, respectively). Although both sexes showed similar specialization pattern throughout their career, earlier specialization was generally evident in female compared to male swimmers. At peak performance age, freestyle was most frequently combined with butterfly. Swimmers who either kept competing in all five swimming strokes or focused on only one at the beginning of their careers showed lowest probability of becoming an international-class swimmer. Physiological variety increased during junior age but declined again to three race distances towards elite age.
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Affiliation(s)
- Dennis-Peter Born
- Swiss Swimming Federation, Section for High-Performance Sports, Bern, Switzerland.
- Department for Elite Sport, Swiss Federal Institute of Sport Magglingen, Hauptstrasse 247, 2532, Magglingen, Switzerland.
| | - Jenny Lorentzen
- Computing in Science, University of Hamburg, Hamburg, Germany
| | - Glenn Björklund
- Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, Sweden
| | - Thomas Stöggl
- Red Bull Athlete Performance Center, Thalgau, Austria
| | - Michael Romann
- Department for Elite Sport, Swiss Federal Institute of Sport Magglingen, Hauptstrasse 247, 2532, Magglingen, Switzerland
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5
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Hilton J, Hall I. A beta-Poisson model for infectious disease transmission. PLoS Comput Biol 2024; 20:e1011856. [PMID: 38330050 PMCID: PMC10903957 DOI: 10.1371/journal.pcbi.1011856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 02/29/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Outbreaks of emerging and zoonotic infections represent a substantial threat to human health and well-being. These outbreaks tend to be characterised by highly stochastic transmission dynamics with intense variation in transmission potential between cases. The negative binomial distribution is commonly used as a model for transmission in the early stages of an epidemic as it has a natural interpretation as the convolution of a Poisson contact process and a gamma-distributed infectivity. In this study we expand upon the negative binomial model by introducing a beta-Poisson mixture model in which infectious individuals make contacts at the points of a Poisson process and then transmit infection along these contacts with a beta-distributed probability. We show that the negative binomial distribution is a limit case of this model, as is the zero-inflated Poisson distribution obtained by combining a Poisson-distributed contact process with an additional failure probability. We assess the beta-Poisson model's applicability by fitting it to secondary case distributions (the distribution of the number of subsequent cases generated by a single case) estimated from outbreaks covering a range of pathogens and geographical settings. We find that while the beta-Poisson mixture can achieve a closer to fit to data than the negative binomial distribution, it is consistently outperformed by the negative binomial in terms of Akaike Information Criterion, making it a suboptimal choice on parsimonious grounds. The beta-Poisson performs similarly to the negative binomial model in its ability to capture features of the secondary case distribution such as overdispersion, prevalence of superspreaders, and the probability of a case generating zero subsequent cases. Despite this possible shortcoming, the beta-Poisson distribution may still be of interest in the context of intervention modelling since its structure allows for the simulation of measures which change contact structures while leaving individual-level infectivity unchanged, and vice-versa.
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Affiliation(s)
- Joe Hilton
- School of Life Sciences and Zeeman Institute (SBIDER), University of Warwick, Coventry, United Kingdom
| | - Ian Hall
- Department of Mathematics and School of Health Sciences, University of Manchester, Manchester, United Kingdom
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6
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Qiao X, He H, Sun L, Bai S, Ye P. Testing latent classes in gut microbiome data using generalized Poisson regression models. Stat Med 2024; 43:102-124. [PMID: 37921025 DOI: 10.1002/sim.9944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 08/11/2023] [Accepted: 09/29/2023] [Indexed: 11/04/2023]
Abstract
Human microbiome research has gained increasing importance due to its critical roles in comprehending human health and disease. Within the realm of microbiome research, the data generated often involves operational taxonomic unit counts, which can frequently present challenges such as over-dispersion and zero-inflation. To address dispersion-related concerns, the generalized Poisson model offers a flexible solution, effectively handling data characterized by over-dispersion, equi-dispersion, and under-dispersion. Furthermore, the realm of zero-inflated generalized Poisson models provides a strategic avenue to simultaneously tackle both over-dispersion and zero-inflation. The phenomenon of zero-inflation frequently stems from the heterogeneous nature of study populations. It emerges when specific microbial taxa fail to thrive in the microbial community of certain subjects, consequently resulting in a consistent count of zeros for these individuals. This subset of subjects represents a latent class, where their zeros originate from the genuine absence of the microbial taxa. In this paper, we introduce a novel testing methodology designed to uncover such latent classes within generalized Poisson regression models. We establish a closed-form test statistic and deduce its asymptotic distribution based on estimating equations. To assess its efficacy, we conduct an extensive array of simulation studies, and further apply the test to detect latent classes in human gut microbiome data from the Bogalusa Heart Study.
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Affiliation(s)
- Xinhui Qiao
- School of Statistics, University of International Business and Economics, Beijing, China
| | - Hua He
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Liuquan Sun
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Shuo Bai
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Peng Ye
- School of Statistics, University of International Business and Economics, Beijing, China
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7
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Sims A, Tiwari H, Levitan EB, Long D, Howard G, Brown T, Smith MJ, Cui J, Long DL. Application of marginalized zero-inflated models when mediators have excess zeroes. Stat Methods Med Res 2024; 33:148-161. [PMID: 38155559 DOI: 10.1177/09622802231220495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Mediation analysis has become increasingly popular over the last decade as researchers are interested in assessing mechanistic pathways for intervention. Although available methods have increased, there are still limited options for mediation analysis with zero-inflated count variables where the distribution of response has a "cluster" of data at the zero value (i.e. distribution of number of cigarettes smoked per day, where nonsmokers cluster at zero cigarettes). The currently available methods do not obtain unbiased population average effects of mediation effects. In this paper, we propose an extension of the counterfactual approach to mediation with direct and indirect effects to scenarios where the mediator is a count variable with excess zeroes by utilizing the Marginalized Zero-Inflated Poisson Model (MZIP) for the mediator model. We derive direct and indirect effects for continuous, binary, and count outcomes, as well as adapt to allow mediator-exposure interactions. Our proposed work allows straightforward calculation of direct and indirect effects for the overall population mean values of the mediator, for scenarios in which researchers are interested in generalizing direct and indirect effects to the population. We apply this novel methodology to an application observing how alcohol consumption may explain sex differences in cholesterol and assess model performance via a simulation study comparing the proposed MZIP mediator framework to existing methods for marginal mediator effects.
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Affiliation(s)
- Andrew Sims
- Department of Biostatistics, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Hemant Tiwari
- Department of Biostatistics, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Emily B Levitan
- Department of Epidemiology, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Dustin Long
- Department of Biostatistics, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - George Howard
- Department of Biostatistics, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Todd Brown
- Department of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Melissa J Smith
- Department of Biostatistics, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - Jinhong Cui
- Department of Biostatistics, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
| | - D Leann Long
- Department of Biostatistics, The University of Alabama at Birmingham School of Public Health, Birmingham, Alabama, USA
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8
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Song CY, Shanechi MM. Unsupervised learning of stationary and switching dynamical system models from Poisson observations. J Neural Eng 2023; 20:066029. [PMID: 38083862 PMCID: PMC10714100 DOI: 10.1088/1741-2552/ad038d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 09/15/2023] [Accepted: 10/16/2023] [Indexed: 12/18/2023]
Abstract
Objective. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanatory power and interpretability by piecing together successive regimes of simpler dynamics to capture more complex ones. However, in many cases, reliable regime labels are not available, thus demanding accurate unsupervised learning methods for Poisson observations. Existing learning methods, however, rely on inference of latent states in neural activity using the Laplace approximation, which may not capture the broader properties of densities and may lead to inaccurate learning. Thus, there is a need for new inference methods that can enable accurate model learning.Approach. To achieve accurate model learning, we derive a novel inference method based on deterministic sampling for Poisson observations called the Poisson Cubature Filter (PCF) and embed it in an unsupervised learning framework. This method takes a minimum mean squared error approach to estimation. Terms that are difficult to find analytically for Poisson observations are approximated in a novel way with deterministic sampling based on numerical integration and cubature rules.Main results. PCF enabled accurate unsupervised learning in both stationary and switching dynamical systems and largely outperformed prior Laplace approximation-based learning methods in both simulations and motor cortical spiking data recorded during a reaching task. These improvements were larger for smaller data sizes, showing that PCF-based learning was more data efficient and enabled more reliable regime identification. In experimental data and unsupervised with respect to behavior, PCF-based learning uncovered interpretable behavior-relevant regimes unlike prior learning methods.Significance. The developed unsupervised learning methods for switching dynamical systems can accurately uncover latent regimes and states in population spiking activity, with important applications in both basic neuroscience and neurotechnology.
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Affiliation(s)
- Christian Y Song
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
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9
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Koslovsky MD. A Bayesian zero-inflated Dirichlet-multinomial regression model for multivariate compositional count data. Biometrics 2023; 79:3239-3251. [PMID: 36896642 DOI: 10.1111/biom.13853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 02/23/2023] [Indexed: 03/11/2023]
Abstract
The Dirichlet-multinomial (DM) distribution plays a fundamental role in modern statistical methodology development and application. Recently, the DM distribution and its variants have been used extensively to model multivariate count data generated by high-throughput sequencing technology in omics research due to its ability to accommodate the compositional structure of the data as well as overdispersion. A major limitation of the DM distribution is that it is unable to handle excess zeros typically found in practice which may bias inference. To fill this gap, we propose a novel Bayesian zero-inflated DM model for multivariate compositional count data with excess zeros. We then extend our approach to regression settings and embed sparsity-inducing priors to perform variable selection for high-dimensional covariate spaces. Throughout, modeling decisions are made to boost scalability without sacrificing interpretability or imposing limiting assumptions. Extensive simulations and an application to a human gut microbiome dataset are presented to compare the performance of the proposed method to existing approaches. We provide an accompanying R package with a user-friendly vignette to apply our method to other datasets.
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Affiliation(s)
- Matthew D Koslovsky
- Department of Statistics, Colorado State University, Fort Collins, Colorado, USA
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10
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Li H, Li C. Multivariate control charts for monitoring a bivariate correlated count process with application to meningococcal disease. Stat Methods Med Res 2023; 32:2299-2317. [PMID: 37881001 DOI: 10.1177/09622802231206476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
In recent years, with the increasing number and complexity of infectious diseases, the idea of using control charts to monitor public health and disease has been proposed. In this paper, we study multivariate control charts for monitoring a bivariate integer-valued autocorrelation process with bivariate Poisson distribution and select the optimal control scheme by comparing the performance of control charts. Furthermore, the meningococcal patient event in two states in Australia serves as an example to illustrate the application of these methods. The results show that the D exponentially weighted moving average control scheme can detect the changes in the mean value faster, which is a significant advantage.
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Affiliation(s)
- Hanhan Li
- School of Mathematics, Jilin University, Changchun, Jilin Province, China
| | - Cong Li
- School of Mathematics, Jilin University, Changchun, Jilin Province, China
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11
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Alonso-Pena M, Gijbels I, Crujeiras RM. Flexible joint modeling of mean and dispersion for the directional tuning of neuronal spike counts. Biometrics 2023; 79:3431-3444. [PMID: 37327387 DOI: 10.1111/biom.13882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/18/2023] [Indexed: 06/18/2023]
Abstract
The study of how the number of spikes in a middle temporal visual area (MT/V5) neuron is tuned to the direction of a visual stimulus has attracted considerable attention over the years, but recent studies suggest that the variability of the number of spikes might also be influenced by the directional stimulus. This entails that Poisson regression models are not adequate for this type of data, as the observations usually present over/underdispersion (or both) with respect to the Poisson distribution. This paper makes use of the double exponential family and presents a flexible model to estimate, jointly, the mean and dispersion functions, accounting for the effect of a circular covariate. The empirical performance of the proposal is explored via simulations and an application to a neurological data set is shown.
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Affiliation(s)
- María Alonso-Pena
- ORSTAT, KU Leuven, Leuven, Belgium
- CITMAga, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Irène Gijbels
- Department of Mathematics and Leuven Statistics Research Center (LStat), KU Leuven, Leuven, Belgium
| | - Rosa M Crujeiras
- CITMAga, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
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12
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Morales FEC. State-space prior distribution for parameter of nonhomogeneous Poisson spatiotemporal models. Biom J 2023; 65:e2200125. [PMID: 37424029 DOI: 10.1002/bimj.202200125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 10/10/2022] [Accepted: 03/23/2023] [Indexed: 07/11/2023]
Abstract
This article proposes a new class of nonhomogeneous Poisson spatiotemporal model. In this approach, we use a state-space model-based prior distribution to handle the scale and shape parameters of the Weibull intensity function. The proposed prior distribution enables the inclusion of changes in the behavior of the intensity function over time. In defining the spatial correlation function of the model, we include anisotropy via spatial deformation. We estimate the model parameters from a Bayesian perspective, employ the Markov chain Monte Carlo approach, and validate this estimation procedure through a simulation exercise. Finally, the extreme rainfall in the southern semiarid region in northeastern Brazil is analyzed using the R10mm index. The proposed model showed better fit and prediction ability than did other nonhomogeneous Poisson spatiotemporal models available in the literature. This improvement in performance is mainly due to the flexibility of the intensity function that is achieved by allowing the incorporation, in time, of the climatic characteristics of this region.
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13
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Lu J, Meyer S. A zero-inflated endemic-epidemic model with an application to measles time series in Germany. Biom J 2023; 65:e2100408. [PMID: 37439440 DOI: 10.1002/bimj.202100408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/24/2023] [Accepted: 06/15/2023] [Indexed: 07/14/2023]
Abstract
Count data with an excess of zeros are often encountered when modeling infectious disease occurrence. The degree of zero inflation can vary over time due to nonepidemic periods as well as by age group or region. A well-established approach to analyze multivariate incidence time series is the endemic-epidemic modeling framework, also known as the HHH approach. However, it assumes Poisson or negative binomial distributions and is thus not tailored to surveillance data with excess zeros. Here, we propose a multivariate zero-inflated endemic-epidemic model with random effects that extends HHH. Parameters of both the zero-inflation probability and the HHH part of this mixture model can be estimated jointly and efficiently via (penalized) maximum likelihood inference using analytical derivatives. We found proper convergence and good coverage of confidence intervals in simulation studies. An application to measles counts in the 16 German states, 2005-2018, showed that zero inflation is more pronounced in the Eastern states characterized by a higher vaccination coverage. Probabilistic forecasts of measles cases improved when accounting for zero inflation. We anticipate zero-inflated HHH models to be a useful extension also for other applications and provide an implementation in an R package.
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Affiliation(s)
- Junyi Lu
- Institute of Medical Informatics, Biometry, and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sebastian Meyer
- Institute of Medical Informatics, Biometry, and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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14
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Shi Y, Li H, Wang C, Chen J, Jiang H, Shih YCT, Zhang H, Song Y, Feng Y, Liu L. A flexible quasi-likelihood model for microbiome abundance count data. Stat Med 2023; 42:4632-4643. [PMID: 37607718 PMCID: PMC11045296 DOI: 10.1002/sim.9880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 07/28/2023] [Accepted: 08/01/2023] [Indexed: 08/24/2023]
Abstract
In this article, we present a flexible model for microbiome count data. We consider a quasi-likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi-likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package "fql" for the application of our method.
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Affiliation(s)
- Yiming Shi
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, New York
| | - Chan Wang
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, New York
| | - Jun Chen
- Division of Computational Biology, Mayo Clinic, Rochester, Minnesota
| | - Hongmei Jiang
- Department of Statistics, Northwestern University, Evanston, Illinois
| | - Ya-Chen T. Shih
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Yizhe Song
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri
| | - Yang Feng
- Department of Biostatistics, College of Global Public Health, New York University, New York, New York
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri
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15
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Mohammadi Z, Bakouch HS, Sharafi M. Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations. Int J Biostat 2023; 19:473-488. [PMID: 36302373 DOI: 10.1515/ijb-2022-0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/24/2022] [Indexed: 11/15/2023]
Abstract
In this paper, we propose the first-order stationary integer-valued autoregressive process with the cosine Poisson innovation, based on the negative binomial thinning operator. It can be equi-dispersed, under-dispersed and over-dispersed. Therefore, it is flexible for modelling integer-valued time series. Some statistical properties of the process are derived. The parameters of the process are estimated by two methods of estimation and the performances of the estimators are evaluated via some simulation studies. Finally, we demonstrate the usefulness of the proposed model by modelling and analyzing some practical count time series data on the daily deaths of COVID-19 and the drug calls data.
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Affiliation(s)
| | - Hassan S Bakouch
- Department of Mathematics, College of Science, Qassim University, Buraydah, Saudi Arabia
- Department of Mathematics, Faculty of Science, Tanta University, Tanta, Egypt
| | - Maryam Sharafi
- Department of Statistics, Shiraz University, Shiraz, Iran
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16
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Rivera D, Arango-Lasprilla JC, Olabarrieta-Landa L. B - 41 Development of Norms for Verbal Fluency Test in Bilinguals Sample: a Generalized Linear Mixed Model with Poisson Distribution Approach. Arch Clin Neuropsychol 2023; 38:1405. [PMID: 37807436 DOI: 10.1093/arclin/acad067.247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Abstract
OBJECTIVE To generate norms for monolinguals (MO) Spanish speakers, Basque bilinguals (BI) and Catalan BIs on verbal fluency tests (VFT). METHOD 89 MOs, 139 Basque BIs, and 132 Catalan BIs completed phonological and semantic VFT in Spanish and Basque or Catalan. Majority of the sample was women (62.2%) with age of 48.5 ± 18.2 and education 13.1 ± 3.8. The participants completed the task in two languages. Two generalized linear mixed models with Poisson distribution (GLMM) were used to evaluate the logarithm of the expected value using fixed effects (region, language, age, age2, education, and sex) and random effects (type of letter/categories, and participant). GLMM was conducted using a long data format with the total number of words as outcome variable. RESULTS A GLMM Poisson for phonological VFT showed a quadratic age, logarithmic of education, region and language effects (ps < 0.001). The random intercepts for type of letter were significant (variance = 0.00875, p-value<0.001), indicating significant differences in total words through letters. Model fit was good (AIC = 24,521, BIC = 24,578). A second GLMM Poisson for semantic VFT showed a quadratic age, logarithmic of education (ps < 0.001) and sex effect (p < 0.01) on the total number of words. The random intercepts for type of category were also significant (variance = 0.02583, p-value<0.001), indicating significant differences in total words through categories. Model fit was good (AIC = 12,128, BIC = 12,185). CONCLUSIONS GLMM Poisson assumes that the response variable follows a Poisson distribution, as in VFT case, and it controls random effects, such as participant and range of letters in a given language. This approach could be adequate for generating norms in neuropsychological tests.
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17
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Turchetta A, Savy N, Stephens DA, Moodie EEM, Klein MB. A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies. Stat Med 2023; 42:4193-4206. [PMID: 37491664 DOI: 10.1002/sim.9855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 07/04/2023] [Accepted: 07/14/2023] [Indexed: 07/27/2023]
Abstract
Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution. However, the constant-rate assumption is a restrictive limitation that is rarely appropriate for applications in real studies. In this paper, we illustrate a flexible generalization of this methodology which allows the enrollment rates to vary over time by modeling them through B-splines. We show the suitability of this approach for a wide range of recruitment behaviors in a simulation study and by estimating the recruitment progression of the Canadian Co-infection Cohort.
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Affiliation(s)
- Armando Turchetta
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Nicolas Savy
- Toulouse Mathematics Institute, University of Toulouse III, Toulouse, France
| | - David A Stephens
- Department of Mathematics and Statistics, McGill University, Montral, Quebec, Canada
| | - Erica E M Moodie
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Marina B Klein
- Department of Medicine, Division of Infectious Diseases/Chronic Viral Illness Service, McGill University Health Center, Montreal, Quebec, Canada
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18
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Hossain A, Lall R, Ji C, Bruce J, Underwood M, Lamb SE. Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT). BMC Med Res Methodol 2023; 23:216. [PMID: 37784050 PMCID: PMC10546684 DOI: 10.1186/s12874-023-02040-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 09/22/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND Fractures are rare events and can occur because of a fall. Fracture counts are distinct from other count data in that these data are positively skewed, inflated by excess zero counts, and events can recur over time. Analytical methods used to assess fracture data and account for these characteristics are limited in the literature. METHODS Commonly used models for count data include Poisson regression, negative binomial regression, hurdle regression, and zero-inflated regression models. In this paper, we compare four alternative statistical models to fit fracture counts using data from a large UK based clinical trial evaluating the clinical and cost-effectiveness of alternative falls prevention interventions in older people (Prevention of Falls Injury Trial; PreFIT). RESULTS The values of Akaike information criterion and Bayesian information criterion, the goodness-of-fit statistics, were the lowest for negative binomial model. The likelihood ratio test of no dispersion in the data showed strong evidence of dispersion (chi-square = 225.68, p-value < 0.001). This indicates that the negative binomial model fits the data better compared to the Poisson regression model. We also compared the standard negative binomial regression and mixed effects negative binomial models. The LR test showed no gain in fitting the data using mixed effects negative binomial model (chi-square = 1.67, p-value = 0.098) compared to standard negative binomial model. CONCLUSIONS The negative binomial regression model was the most appropriate and optimal fit model for fracture count analyses. TRIAL REGISTRATION The PreFIT trial was registered as ISRCTN71002650.
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Affiliation(s)
- Anower Hossain
- Warwick Clinical Trials Unit, University of Warwick, Coventry, UK.
- Institute of Statistical Research and Training (ISRT), University of Dhaka, Dhaka, Bangladesh.
| | - Ranjit Lall
- Warwick Clinical Trials Unit, University of Warwick, Coventry, UK
| | - Chen Ji
- Warwick Clinical Trials Unit, University of Warwick, Coventry, UK
| | - Julie Bruce
- Warwick Clinical Trials Unit, University of Warwick, Coventry, UK
- University Hospital Coventry and Warwickshire NHS Trust, Clifford Bridge Road, Coventry, UK
| | - Martin Underwood
- Warwick Clinical Trials Unit, University of Warwick, Coventry, UK
- University Hospital Coventry and Warwickshire NHS Trust, Clifford Bridge Road, Coventry, UK
| | - Sarah E Lamb
- University of Exeter, St Luke's Campus, Exeter, UK
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19
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Arsenović D, Lužanin Z, Milošević D, Dunjić J, Nikitović V, Savić S. The effects of summer ambient temperature on total mortality in Serbia. Int J Biometeorol 2023; 67:1581-1589. [PMID: 37453990 DOI: 10.1007/s00484-023-02520-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 06/02/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
In the context of recent climate change, temperature-attributable mortality has become an important public health threat worldwide. A large number of studies in Europe have identified a relationship between temperature and mortality, while only a limited number of scholars provided evidence for Serbia. In order to provide more evidence for better management of health resources at the regional and local level, this study aims to assess the impact of summer temperature on the population in Serbia, using daily average temperature (Ta) and mortality (CDR (crude death rate) per 100,000). The analysis was done for five areas (Belgrade, Novi Sad, Niš, Loznica, and Vranje), covering the summer (June-August) period of 2001-2015. In order to quantify the Ta-related CDR, a generalized additive model (GAM) assuming a quasi-Poisson distribution with log as the link function was used. Five regression models were constructed, for each area, revealing a statistically significant positive relationship between Ta and CDR in four areas. The effect of Ta on CDR was defined as the relative risk (RR), which was obtained as the exponential regression coefficient of the models. RR indicates that a 1 °C increase in Ta at lag0 was associated with an increase in CDR of 1.7% for Belgrade, Novi Sad, and Niš and 2% for Loznica. The model for Vranje did not quantify a statistically significant increase in CDR due to Ta (RR=1.006, 95% CI 0.991-1.020). Similar results were confirmed for gender, with a slightly higher risk for women. Analysis across lag structure showed different exposure, but the highest effect of Ta mainly occurs over the short term and persists for 3 days.
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Affiliation(s)
- Daniela Arsenović
- Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, Novi Sad, 21000, Serbia.
| | - Zorana Lužanin
- Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, Novi Sad, 21000, Serbia
| | - Dragan Milošević
- Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, Novi Sad, 21000, Serbia
| | - Jelena Dunjić
- Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, Novi Sad, 21000, Serbia
| | - Vladimir Nikitović
- Institute of Social Sciences, Kraljice Natalije 45, Belgrade, 11000, Serbia
| | - Stevan Savić
- Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, Novi Sad, 21000, Serbia
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20
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Cui T, Wang T. A comprehensive assessment of hurdle and zero-inflated models for single cell RNA-sequencing analysis. Brief Bioinform 2023; 24:bbad272. [PMID: 37507115 PMCID: PMC10516395 DOI: 10.1093/bib/bbad272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/17/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
Single cell RNA-sequencing (scRNA-seq) technology has significantly advanced the understanding of transcriptomic signatures. Although various statistical models have been used to describe the distribution of gene expression across cells, a comprehensive assessment of the different models is missing. Moreover, the growing number of features associated with scRNA-seq datasets creates new challenges for analytical accuracy and computing speed. Here, we developed a Python-based package (TensorZINB) to solve the zero-inflated negative binomial (ZINB) model using the TensorFlow deep learning framework. We used a sequential initialization method to solve the numerical stability issues associated with hurdle and zero-inflated models. A recursive feature selection protocol was used to optimize feature selections for data processing and downstream differentially expressed gene (DEG) analysis. We proposed a class of hybrid models combining nested models to further improve the model's performance. Additionally, we developed a new method to convert a continuous distribution to its equivalent discrete form, so that statistical models can be fairly compared. Finally, we showed that the proposed TensorFlow algorithm (TensorZINB) was numerically stable and that its computing speed and performance were superior to those of existing ZINB solvers. Moreover, we implemented seven hurdle and zero-inflated statistical models in Python and systematically assessed their performance using a real scRNA-seq dataset. We demonstrated that the ZINB model achieved the lowest Akaike information criterion compared with other models tested. Taken together, TensorZINB was accurate, efficient and scalable for the implementation of ZINB and for large-scale scRNA-seq data analysis with DEG identification.
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Affiliation(s)
- Tao Cui
- Department of Pharmacology and Physiology Georgetown University Medical Center SE407 Med/Dent 3900 Reservoir Road, N.W. Washington D.C., USA
| | - Tingting Wang
- Department of Pharmacology and Physiology Georgetown University Medical Center SE407 Med/Dent 3900 Reservoir Road, N.W. Washington D.C., USA
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21
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Ge L, Liang B, Hu T, Sun J, Zhao S, Li Y. Variable selection for mixed panel count data under the proportional mean model. Stat Methods Med Res 2023; 32:1728-1748. [PMID: 37401336 DOI: 10.1177/09622802231184637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Mixed panel count data have attracted increasing attention in medical research based on event history studies. When such data arise, one either observes the number of event occurrences or only knows whether the event has happened or not over an observation period. In this article, we discuss variable selection in event history studies given such complex data, for which there does not seem to exist an established procedure. For the problem, we propose a penalized likelihood variable selection procedure and for the implementation, an expectation-maximization algorithm is developed with the use of the coordinate descent algorithm in the M-step. Furthermore, the oracle property of the proposed method is established, and a simulation study is performed and indicates that the proposed method works well in practical scenarios. Finally, the method is applied to identify the risk factors associated with medical non-adherence arising from the Sequenced Treatment Alternatives to Relieve Depression Study.
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Affiliation(s)
- Lei Ge
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Baosheng Liang
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Tao Hu
- School of Mathematical Sciences, Capital Normal University, Beijing, China
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, MO, USA
| | - Shishun Zhao
- Applied Statistical Research Center, School of Mathematics, Jilin University, Changchun, China
| | - Yang Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
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22
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Yu W, Gargett T, Du Z. A Poisson distribution-based general model of cancer rates and a cancer risk-dependent theory of aging. Aging (Albany NY) 2023; 15:8537-8551. [PMID: 37659107 PMCID: PMC10522393 DOI: 10.18632/aging.205016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/20/2023] [Indexed: 09/04/2023]
Abstract
This article presents a formula for modeling the lifetime incidence of cancer in humans. The formula utilizes a Poisson distribution-based "np" model to predict cancer incidence, with "n" representing the effective number of cell turnover and "p" representing the probability of single-cell transformation. The model accurately predicts the observed incidence of cancer in humans when a reduction in cell turnover due to aging is taken into account. The model also suggests that cancer development is ultimately inevitable. The article proposes a theory of aging based on this concept, called the "np" theory. According to this theory, an organism maintains its order by balancing cellular entropy through continuous proliferation. However, cellular "information entropy" in the form of accumulated DNA mutations increases irreversibly over time, restricting the total number of cells an organism can generate throughout its lifetime. When cell division slows down and fails to compensate for the increased entropy in the system, aging occurs. Essentially, aging is the phenomenon of running out of predetermined cell resources. Different species have evolved separate strategies to utilize their limited cell resources throughout their life cycle.
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Affiliation(s)
- Wenbo Yu
- Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, Australia
- Cancer Clinical Trials Unit, Royal Adelaide Hospital, Adelaide, SA, Australia
- School of Medicine, The University of Adelaide, Adelaide, SA, Australia
| | - Tessa Gargett
- Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, Australia
- Cancer Clinical Trials Unit, Royal Adelaide Hospital, Adelaide, SA, Australia
- School of Medicine, The University of Adelaide, Adelaide, SA, Australia
| | - Zhenglong Du
- Department of Molecular and Biomedical Science, School of Biological Sciences, The University of Adelaide, Adelaide, SA, Australia
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23
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Cho H, Liu C, Preisser JS, Wu D. A bivariate zero-inflated negative binomial model and its applications to biomedical settings. Stat Methods Med Res 2023; 32:1300-1317. [PMID: 37167422 PMCID: PMC10500952 DOI: 10.1177/09622802231172028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The zero-inflated negative binomial distribution has been widely used for count data analyses in various biomedical settings due to its capacity of modeling excess zeros and overdispersion. When there are correlated count variables, a bivariate model is essential for understanding their full distributional features. Examples include measuring correlation of two genes in sparse single-cell RNA sequencing data and modeling dental caries count indices on two different tooth surface types. For these purposes, we develop a richly parametrized bivariate zero-inflated negative binomial model that has a simple latent variable framework and eight free parameters with intuitive interpretations. In the scRNA-seq data example, the correlation is estimated after adjusting for the effects of dropout events represented by excess zeros. In the dental caries data, we analyze how the treatment with Xylitol lozenges affects the marginal mean and other patterns of response manifested in the two dental caries traits. An R package "bzinb" is available on Comprehensive R Archive Network.
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Affiliation(s)
- Hunyong Cho
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
| | - Chuwen Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
| | - John S Preisser
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
| | - Di Wu
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, NC, USA
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24
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Alkhairy I. Classical and Bayesian inference for the discrete Poisson Ramos-Louzada distribution with application to COVID-19 data. Math Biosci Eng 2023; 20:14061-14080. [PMID: 37679125 DOI: 10.3934/mbe.2023628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
The present study is based on the derivation of a new extension of the Poisson distribution using the Ramos-Louzada distribution. Several statistical properties of the new distribution are derived including, factorial moments, moment-generating function, probability moments, skewness, kurtosis, and dispersion index. Some reliability properties are also derived. The model parameter is estimated using different classical estimation techniques. A comprehensive simulation study was used to identify the best estimation method. Bayesian estimation with a gamma prior is also utilized to estimate the parameter. Three examples were used to demonstrate the utility of the proposed model. These applications revealed that the PRL-based model outperforms certain existing competing one-parameter discrete models such as the discrete Rayleigh, Poisson, discrete inverted Topp-Leone, discrete Pareto and discrete Burr-Hatke distributions.
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Affiliation(s)
- Ibrahim Alkhairy
- Department of Mathematics, Al-Qunfudah University College, Umm Al-Qura University, Mecca, Saudi Arabia
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25
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Pan Y, Landis JT, Moorad R, Wu D, Marron JS, Dittmer DP. The Poisson distribution model fits UMI-based single-cell RNA-sequencing data. BMC Bioinformatics 2023; 24:256. [PMID: 37330471 PMCID: PMC10276395 DOI: 10.1186/s12859-023-05349-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/24/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND Modeling of single cell RNA-sequencing (scRNA-seq) data remains challenging due to a high percentage of zeros and data heterogeneity, so improved modeling has strong potential to benefit many downstream data analyses. The existing zero-inflated or over-dispersed models are based on aggregations at either the gene or the cell level. However, they typically lose accuracy due to a too crude aggregation at those two levels. RESULTS We avoid the crude approximations entailed by such aggregation through proposing an independent Poisson distribution (IPD) particularly at each individual entry in the scRNA-seq data matrix. This approach naturally and intuitively models the large number of zeros as matrix entries with a very small Poisson parameter. The critical challenge of cell clustering is approached via a novel data representation as Departures from a simple homogeneous IPD (DIPD) to capture the per-gene-per-cell intrinsic heterogeneity generated by cell clusters. Our experiments using real data and crafted experiments show that using DIPD as a data representation for scRNA-seq data can uncover novel cell subtypes that are missed or can only be found by careful parameter tuning using conventional methods. CONCLUSIONS This new method has multiple advantages, including (1) no need for prior feature selection or manual optimization of hyperparameters; (2) flexibility to combine with and improve upon other methods, such as Seurat. Another novel contribution is the use of crafted experiments as part of the validation of our newly developed DIPD-based clustering pipeline. This new clustering pipeline is implemented in the R (CRAN) package scpoisson.
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Affiliation(s)
- Yue Pan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Justin T Landis
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Razia Moorad
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Di Wu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Adam School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - J S Marron
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Dirk P Dittmer
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, USA.
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26
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Pittman B, Buta E, Garrison K, Gueorguieva R. Models for Zero-Inflated and Overdispersed Correlated Count Data: An Application to Cigarette Use. Nicotine Tob Res 2023; 25:996-1003. [PMID: 36318799 PMCID: PMC10077942 DOI: 10.1093/ntr/ntac253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 09/02/2022] [Accepted: 10/31/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Count outcomes in tobacco research are often analyzed with the Poisson distribution. However, they often exhibit features such as overdispersion (variance larger than expected) and zero inflation (extra zeros) that violate model assumptions. Furthermore, longitudinal studies have repeated measures that generate correlated counts. Failure to account for overdispersion, zero inflation, and correlation can yield incorrect statistical inferences. Thus, it is important to familiarize researchers with proper models for such data. AIMS AND METHODS Poisson and Negative Binomial models with correlated random effects with and without zero inflation are presented. The illustrative data comes from a study comparing a mindfulness training app (Craving to Quit [C2Q], n = 60) with a control app (experience sampling-only app, n = 66) on smoking frequency at 1, 3, and 6 months. Predictors include app, time, the app-by-time interaction, and baseline smoking. Each model is evaluated in terms of accounting for zero inflation, overdispersion, and correlation in the data. Emphasis is placed on evaluating model fit, subject-specific interpretation of effects, and choosing an appropriate model. RESULTS The hurdle Poisson model provided the best fit to the data. Smoking abstinence rates were 33%, 32%, and 28% at 1, 3, and 6 months, respectively, with variance larger than expected by a factor >7 at each follow-up. Individuals on C2Q were less likely to achieve abstinence across time but likely to smoke fewer cigarettes if smoking. CONCLUSIONS The models presented are specifically suited for analyzing correlated count outcomes and account for zero inflation and overdispersion. We provide guidance to researchers on the use of these models to better inform nicotine and tobacco research. IMPLICATIONS In tobacco research, count outcomes are often measured repeatedly on the same subject and thus correlated. Such outcomes often have many zeros and exhibit large variances relative to the mean. Analyzing such data require models specifically suited for correlated counts. The presented models and guidelines could improve the rigor of the analysis of correlated count data and thus increase the impact of studies in nicotine and tobacco research using such outcomes.
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Affiliation(s)
- Brian Pittman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Eugenia Buta
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Kathleen Garrison
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Ralitza Gueorguieva
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Lydersen S. Poissonfordelingen for antall hendelser. Tidsskr Nor Laegeforen 2023; 143:22-0701. [PMID: 36655961 DOI: 10.4045/tidsskr.22.0701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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28
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Fujikawa H. [The Validity of the Poisson Distribution to Analyze Microbial Colony Counts on Agar Plates for Food Samples]. Shokuhin Eiseigaku Zasshi 2023; 64:174-178. [PMID: 37880096 DOI: 10.3358/shokueishi.64.174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Microbial colony counts of food samples in microbiological examinations are one of the most important items. The probability distributions for the colony counts per agar plate at the dilution of counting had not been intensively studied so far. Recently we analyzed the colony counts of food samples with several probability distributions using the Pearson's chi-square value by the "traditional" statistics as the index of fit [Fujikawa and Tsubaki, Food Hyg.Saf.Sc., 60, 88-95 (2019)]. As a result, the selected probability distributions depended on the samples. In this study we newly selected a probability distribution, namely a statistical model, suitable for the above data with the method of maximum likelihood from the probabilistic point of view. The Akaike's Information Criterion (AIC) was used as the index of fit. Consequently, the Poisson model were better than the negative binomial model for all of four food samples. The Poisson model was also better than the binomial for three of four microbial culture samples. With Baysian Information Criterion (BIC), the Poisson model was also better than these two models for all the samples. These results suggested that the Poisson distribution would be the best model to estimate the colony counts of food samples. The present study would be the first report on the statistical model selection for the colony counts of food samples with AIC and BIC.
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Affiliation(s)
- Hiroshi Fujikawa
- Laboratory of Veterinary Public Health, Faculty of Agriculture, Tokyo University of Agriculture and Technology
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29
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Gómez YM, Gallardo DI, Bourguignon M, Bertolli E, Calsavara VF. A general class of promotion time cure rate models with a new biological interpretation. Lifetime Data Anal 2023; 29:66-86. [PMID: 36114312 DOI: 10.1007/s10985-022-09575-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 08/30/2022] [Indexed: 06/15/2023]
Abstract
Over the last decades, the challenges in survival models have been changing considerably and full probabilistic modeling is crucial in many medical applications. Motivated from a new biological interpretation of cancer metastasis, we introduce a general method for obtaining more flexible cure rate models. The proposal model extended the promotion time cure rate model. Furthermore, it includes several well-known models as special cases and defines many new special models. We derive several properties of the hazard function for the proposed model and establish mathematical relationships with the promotion time cure rate model. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Simulation studies are conducted to evaluate its performance with a discussion of the obtained results. A real dataset from population-based study of incident cases of melanoma diagnosed in the state of São Paulo, Brazil, is discussed in detail.
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Affiliation(s)
- Yolanda M Gómez
- Departamento de Medicina, Facultad de Medicina, Universidad de Atacama, Copiapó, Chile
| | - Diego I Gallardo
- Departamento de Medicina, Facultad de Medicina, Universidad de Atacama, Copiapó, Chile
- Departamento de Matemática, Facultad de Ingeniería, Universidad de Atacama, Copiapó, Chile
| | - Marcelo Bourguignon
- Departamento de Estatística, Universidade Federal do Rio Grande do Norte, Natal, RN, 59078-970, Brazil.
| | - Eduardo Bertolli
- Skin Cancer Department, A.C.Camargo Cancer Center, São Paulo, SP, Brazil
- Oncology Center, Beneficência Portuguesa, São Paulo, SP, Brazil
| | - Vinicius F Calsavara
- Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Lee MJ, Kim JH, Goh KI, Lee SH, Son SW, Lee DS. Degree distributions under general node removal: Power-law or Poisson? Phys Rev E 2022; 106:064309. [PMID: 36671153 DOI: 10.1103/physreve.106.064309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 11/01/2022] [Indexed: 06/17/2023]
Abstract
Perturbations made to networked systems may result in partial structural loss, such as a blackout in a power-grid system. Investigating the resulting disturbance in network properties is quintessential to understand real networks in action. The removal of nodes is a representative disturbance, but previous studies are seemingly contrasting about its effect on arguably the most fundamental network statistic, the degree distribution. The key question is about the functional form of the degree distributions that can be altered during node removal or sampling. The functional form is decisive in the remaining subnetwork's static and dynamical properties. In this work, we clarify the situation by utilizing the relative entropies with respect to the reference distributions in the Poisson and power-law form, to quantify the distance between the subnetwork's degree distribution and either of the reference distributions. Introducing general sequential node removal processes with continuously different levels of hub protection to encompass a series of scenarios including uniform random removal and preferred or protective (i.e., biased random) removal of the hub, we classify the altered degree distributions starting from various power-law forms by comparing two relative entropy values. From the extensive investigation in various scenarios based on direct node-removal simulations and by solving the rate equation of degree distributions, we discover in the parameter space two distinct regimes, one where the degree distribution is closer to the power-law reference distribution and the other closer to the Poisson distribution.
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Affiliation(s)
- Mi Jin Lee
- Department of Applied Physics, Hanyang University, Ansan 15588, Korea
| | - Jung-Ho Kim
- Department of Physics, Korea University, Seoul 02841, Korea
| | - Kwang-Il Goh
- Department of Physics, Korea University, Seoul 02841, Korea
| | - Sang Hoon Lee
- Department of Physics and Research Institute of Natural Science, Gyeongsang National University, Jinju 52828, Korea
- Future Convergence Technology Research Institute, Gyeongsang National University, Jinju 52849, Korea
| | - Seung-Woo Son
- Department of Applied Physics, Hanyang University, Ansan 15588, Korea
| | - Deok-Sun Lee
- School of Computational Sciences and Center for AI and Natural Sciences, Korea Institute for Advanced Study, Seoul 02455, Korea
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31
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Ye P, Qiao X, Tang W, Wang C, He H. Testing latent class of subjects with structural zeros in negative binomial models with applications to gut microbiome data. Stat Methods Med Res 2022; 31:2237-2254. [PMID: 35899309 DOI: 10.1177/09622802221115881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Human microbiome research has become a hot-spot in health and medical research in the past decade due to the rapid development of modern high-throughput. Typical data in a microbiome study consisting of the operational taxonomic unit counts may have over-dispersion and/or structural zero issues. In such cases, negative binomial models can be applied to address the over-dispersion issue, while zero-inflated negative binomial models can be applied to address both issues. In practice, it is essential to know if there is zero-inflation in the data before applying negative binomial or zero-inflated negative binomial models because zero-inflated negative binomial models may be unnecessarily complex and difficult to interpret, or may even suffer from convergence issues if there is no zero-inflation in the data. On the other hand, negative binomial models may yield invalid inferences if the data does exhibit excessive zeros. In this paper, we develop a new test for detecting zero-inflation resulting from a latent class of subjects with structural zeros in a negative binomial regression model by directly comparing the amount of observed zeros with what would be expected under the negative binomial regression model. A closed form of the test statistic as well as its asymptotic properties are derived based on estimating equations. Intensive simulation studies are conducted to investigate the performance of the new test and compare it with the classical Wald, likelihood ratio, and score tests. The tests are also applied to human gut microbiome data to test latent class in microbial genera.
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Affiliation(s)
- Peng Ye
- School of Statistics, 12630University of International Business and Economics, Beijing, China
- Department of Epidemiology, 25812School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Xinhui Qiao
- School of Statistics, 12630University of International Business and Economics, Beijing, China
| | - Wan Tang
- Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Chunyi Wang
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hua He
- Department of Epidemiology, 25812School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
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Beisemann M. A flexible approach to modelling over-, under- and equidispersed count data in IRT: The Two-Parameter Conway-Maxwell-Poisson Model. Br J Math Stat Psychol 2022; 75:411-443. [PMID: 35678959 DOI: 10.1111/bmsp.12273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 04/05/2022] [Indexed: 06/01/2023]
Abstract
Several psychometric tests and self-reports generate count data (e.g., divergent thinking tasks). The most prominent count data item response theory model, the Rasch Poisson Counts Model (RPCM), is limited in applicability by two restrictive assumptions: equal item discriminations and equidispersion (conditional mean equal to conditional variance). Violations of these assumptions lead to impaired reliability and standard error estimates. Previous work generalized the RPCM but maintained some limitations. The two-parameter Poisson counts model allows for varying discriminations but retains the equidispersion assumption. The Conway-Maxwell-Poisson Counts Model allows for modelling over- and underdispersion (conditional mean less than and greater than conditional variance, respectively) but still assumes constant discriminations. The present work introduces the Two-Parameter Conway-Maxwell-Poisson (2PCMP) model which generalizes these three models to allow for varying discriminations and dispersions within one model, helping to better accommodate data from count data tests and self-reports. A marginal maximum likelihood method based on the EM algorithm is derived. An implementation of the 2PCMP model in R and C++ is provided. Two simulation studies examine the model's statistical properties and compare the 2PCMP model to established models. Data from divergent thinking tasks are reanalysed with the 2PCMP model to illustrate the model's flexibility and ability to test assumptions of special cases.
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López Vázquez PC, Sánchez González G, Martínez Ortega J, Arroyo Duarte RS. Stochastic epidemiological model: Simulations of the SARS-CoV-2 spreading in Mexico. PLoS One 2022; 17:e0275216. [PMID: 36173956 PMCID: PMC9521938 DOI: 10.1371/journal.pone.0275216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 09/12/2022] [Indexed: 01/08/2023] Open
Abstract
In this paper we model the spreading of the SARS-CoV-2 in Mexico by introducing a new stochastic approximation constructed from first principles, where the number of new infected individuals caused by a single infectious individual per unit time (a day), is a random variable of a time-dependent Poisson distribution. The model, structured on the basis of a Latent-Infectious-(Recovered or Deceased) (LI(RD)) compartmental approximation together with a modulation of the mean number of new infections (the Poisson parameters), provides a good tool to study theoretical and real scenarios.
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Affiliation(s)
- Pablo Carlos López Vázquez
- Departamento de Ciencias Naturales y Exactas, Universidad de Guadalajara, Ameca, Jalisco, México
- * E-mail: (GSG); (PCLV)
| | - Gilberto Sánchez González
- Centro de Investigación Sobre Enfermedades Infecciosas, Instituto Nacional de Salud Pública, Cuernavaca, Morelos, México
- * E-mail: (GSG); (PCLV)
| | - Jorge Martínez Ortega
- Coordinación General de Innovación Gubernamental, Gobierno del Estado de Jalisco, Ciudad Creativa Digital, Guadalajara, Jalisco, México
| | - Renato Salomón Arroyo Duarte
- Coordinación de Análisis Estratégico, Gobierno del Estado de Jalisco, Ciudad Creativa Digital, Guadalajara, Jalisco, México
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Holling H, Jansen K, Böhning W, Böhning D, Martin S, Sangnawakij P. Estimation of Effect Heterogeneity in Rare Events Meta-Analysis. Psychometrika 2022; 87:1081-1102. [PMID: 35133554 PMCID: PMC9433364 DOI: 10.1007/s11336-021-09835-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/14/2021] [Indexed: 06/14/2023]
Abstract
The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.
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Affiliation(s)
- Heinz Holling
- Institute of Psychology, University of Münster, Fliednerstr. 21, 48149, Münster, Germany.
| | - Katrin Jansen
- Institute of Psychology, University of Münster, Fliednerstr. 21, 48149, Münster, Germany
| | - Walailuck Böhning
- Institute of Psychology, University of Münster, Fliednerstr. 21, 48149, Münster, Germany
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Garre A, Zwietering MH, van Boekel MAJS. The Most Probable Curve method - A robust approach to estimate kinetic models from low plate count data resulting in reduced uncertainty. Int J Food Microbiol 2022; 380:109871. [PMID: 35985079 DOI: 10.1016/j.ijfoodmicro.2022.109871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/03/2022] [Accepted: 08/06/2022] [Indexed: 11/19/2022]
Abstract
A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the "true" microbial concentration according to the model, the "actual" concentration in the media considering chance, and the actual counts on the plate. It is based on the assumptions that stress resistance is homogeneous within a microbial population, and that there is no aggregation of microbial cells. Under these assumptions, the number of colonies in/on a plate follows a Poisson distribution with expected value depending on the proposed kinetic model, the number of dilutions and the plated volume. The novel method is compared against (non)linear regression based on a normal likelihood distribution (traditional method), Poisson regression and gamma-Poisson regression using data on the inactivation of Listeria monocytogenes. The conclusion is that the traditional method has limitations when the data includes plates with low (or zero) cell counts, which can be mitigated using more complex (discrete) likelihoods. However, Poisson regression uses an unrealistic likelihood function, making it unsuitable for survivor curves with several log-reductions. Gamma-Poisson regression uses a more realistic likelihood function, even though it is based mostly on empirical hypotheses. We conclude that the MPC method can be used reliably, especially when the data includes plates with low or zero counts. Furthermore, it generates a more realistic description of uncertainty, integrating the contribution of the plating error and reducing the uncertainty of the primary model parameters. Consequently, although it increases modelling complexity, the MPC method can be of great interest in predictive microbiology, especially in studies focused on variability analysis.
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Affiliation(s)
- Alberto Garre
- Food Microbiology, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, the Netherlands
| | - Marcel H Zwietering
- Food Microbiology, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, the Netherlands
| | - Martinus A J S van Boekel
- Food Quality & Design, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, the Netherlands.
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36
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Zrelak PA. Use of the Poisson Distribution Is a Helpful Tool That Is Underused in Nursing Practice. J Nurs Care Qual 2022; 37:E54-E57. [PMID: 34935732 DOI: 10.1097/ncq.0000000000000612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND The Poisson distribution is used to find the probability of an event occurring over an interval of time, distance, area, or volume. PROBLEM It is a helpful statistical tool, especially when evaluating rare events, and is underused in nursing practice. APPROACH A single-group study design is used to demonstrate use of the Poisson distribution in determining whether a change in the number of discrete events is due to random variation or reflects a change in practice patterns and in determining the probability of seeing the number of observed events. OUTCOMES Steps demonstrate how one can easily use the Poisson distribution to answer common questions. CONCLUSION Use of the Poisson distribution can help nurses make better informed decisions about observed variations in care, especially when the data are not normally distributed, and can prevent undue concern when fluctuations in the number of events are associated with random fluctuations.
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Li Y, Oravecz Z, Zhou S, Bodovski Y, Barnett IJ, Chi G, Zhou Y, Friedman NP, Vrieze SI, Chow SM. Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates. Psychometrika 2022; 87:376-402. [PMID: 35076813 PMCID: PMC9177551 DOI: 10.1007/s11336-021-09831-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/25/2021] [Indexed: 05/25/2023]
Abstract
In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.
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Affiliation(s)
- Yanling Li
- Department of Agricultural Economics, Sociology, and Education, The Pennsylvania State University, PA 16802, State College, USA.
| | - Zita Oravecz
- Department of Agricultural Economics, Sociology, and Education, The Pennsylvania State University, PA 16802, State College, USA
| | - Shuai Zhou
- Department of Agricultural Economics, Sociology, and Education, The Pennsylvania State University, PA 16802, State College, USA
| | - Yosef Bodovski
- Department of Agricultural Economics, Sociology, and Education, The Pennsylvania State University, PA 16802, State College, USA
| | - Ian J Barnett
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Guangqing Chi
- Department of Agricultural Economics, Sociology, and Education, The Pennsylvania State University, PA 16802, State College, USA
| | - Yuan Zhou
- Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, USA
| | - Scott I Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Sy-Miin Chow
- Department of Agricultural Economics, Sociology, and Education, The Pennsylvania State University, PA 16802, State College, USA
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Koyama AK, Cheng YJ, Brinks R, Xie H, Gregg EW, Hoyer A, Pavkov ME, Imperatore G. Trends in lifetime risk and years of potential life lost from diabetes in the United States, 1997–2018. PLoS One 2022; 17:e0268805. [PMID: 35609056 PMCID: PMC9129010 DOI: 10.1371/journal.pone.0268805] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/09/2022] [Indexed: 12/04/2022] Open
Abstract
Background Both incidence and mortality of diagnosed diabetes have decreased over the past decade. However, the impact of these changes on key metrics of diabetes burden–lifetime risk (LR), years of potential life lost (YPLL), and years spent with diabetes–is unknown. Methods We used data from 653,811 adults aged ≥18 years from the National Health Interview Survey, a cross-sectional sample of the civilian non-institutionalized population in the United States. LR, YPLL, and years spent with diabetes were estimated from age 18 to 84 by survey period (1997–1999, 2000–2004, 2005–2009, 2010–2014, 2015–2018). The age-specific incidence of diagnosed diabetes and mortality were estimated using Poisson regression. A multistate difference equation accounting for competing risks was used to model each metric. Results LR and years spent with diabetes initially increased then decreased over the most recent time periods. LR for adults at age 20 increased from 31.7% (95% CI: 31.2–32.1%) in 1997–1999 to 40.7% (40.2–41.1%) in 2005–2009, then decreased to 32.8% (32.4–33.2%) in 2015–2018. Both LR and years spent with diabetes were markedly higher among adults of non-Hispanic Black, Hispanic, and other races compared to non-Hispanic Whites. YPLL significantly decreased over the study period, with the estimated YPLL due to diabetes for an adult aged 20 decreasing from 8.9 (8.7–9.1) in 1997–1999 to 6.2 (6.1–6.4) in 2015–2018 (p = 0.02). Conclusion In the United States, diabetes burden is declining, but disparities by race/ethnicity remain. LR remains high with approximately one-third of adults estimated to develop diabetes during their lifetime.
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Affiliation(s)
- Alain K. Koyama
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
- * E-mail:
| | - Yiling J. Cheng
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Ralph Brinks
- Institute for Biometry and Epidemiology, German Diabetes Center, Düsseldorf, Germany
- Medical Biometry and Epidemiology, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Hui Xie
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Edward W. Gregg
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
| | - Annika Hoyer
- Biostatistics and Medical Biometry, Medical School OWL, Bielefeld University, Bielefeld, Germany
| | - Meda E. Pavkov
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Giuseppina Imperatore
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
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Li X, Tong X, Yeung WWY, Kuan P, Yum SHH, Chui CSL, Lai FTT, Wan EYF, Wong CKH, Chan EWY, Lau CS, Wong ICK. Two-dose COVID-19 vaccination and possible arthritis flare among patients with rheumatoid arthritis in Hong Kong. Ann Rheum Dis 2022; 81:564-568. [PMID: 34686479 PMCID: PMC8550868 DOI: 10.1136/annrheumdis-2021-221571] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 10/13/2021] [Indexed: 01/16/2023]
Abstract
OBJECTIVES To investigate the relationship between COVID-19 full vaccination (two completed doses) and possible arthritis flare. METHODS Patients with rheumatoid arthritis (RA) were identified from population-based electronic medical records with vaccination linkage and categorised into BNT162b2 (mRNA vaccine), CoronaVac (inactive virus vaccine) and non-vaccinated groups. The risk of possible arthritis flare after vaccination was compared using a propensity-weighted cohort study design. We defined possible arthritis flare as hospitalisation and outpatient consultation related to RA or reactive arthritis, based on diagnosis records during the episode. Weekly prescriptions of rheumatic drugs since the launch of COVID-19 vaccination programme were compared to complement the findings from a diagnosis-based analysis. RESULTS Among 5493 patients with RA (BNT162b2: 653; CoronaVac: 671; non-vaccinated: 4169), propensity-scored weighted Poisson regression showed no significant association between arthritis flare and COVID-19 vaccination ((BNT162b2: adjusted incidence rate ratio 0.86, 95% Confidence Interval 0.73 to 1.01); CoronaVac: 0.87 (0.74 to 1.02)). The distribution of weekly rheumatic drug prescriptions showed no significant differences among the three groups since the launch of the mass vaccination programme (all p values >0.1 from Kruskal-Wallis test). CONCLUSIONS Current evidence does not support that full vaccination of mRNA or inactivated virus COVID-19 vaccines is associated with possible arthritis flare.
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Affiliation(s)
- Xue Li
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Hong Kong Science and Technology Park, Laboratory of Data Discovery for Health, Hong Kong, China
| | - Xinning Tong
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Winnie Wan Yin Yeung
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Peng Kuan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Samson Hin Hei Yum
- Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Celine Sze Ling Chui
- Hong Kong Science and Technology Park, Laboratory of Data Discovery for Health, Hong Kong, China
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Francisco Tsz Tsun Lai
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Hong Kong Science and Technology Park, Laboratory of Data Discovery for Health, Hong Kong, China
| | - Eric Yuk Fai Wan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Hong Kong Science and Technology Park, Laboratory of Data Discovery for Health, Hong Kong, China
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Carlos King Ho Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Hong Kong Science and Technology Park, Laboratory of Data Discovery for Health, Hong Kong, China
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Esther Wai Yin Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Hong Kong Science and Technology Park, Laboratory of Data Discovery for Health, Hong Kong, China
- Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, China
| | - Chak Sing Lau
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ian Chi Kei Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Hong Kong Science and Technology Park, Laboratory of Data Discovery for Health, Hong Kong, China
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Expert Committee on Clinical Events Assessment Following COVID-19 Immunization, Department of Health, The Government of the Hong Kong Special Administrative Region, Hong Kong, China
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Cho SJ, Naveiras M, Barton E. Modeling Multivariate Count Time Series Data with a Vector Poisson Log-Normal Additive Model: Applications to Testing Treatment Effects in Single-Case Designs. Multivariate Behav Res 2022; 57:422-440. [PMID: 33476178 DOI: 10.1080/00273171.2020.1860732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In education and psychology, single-case designs (SCDs) have been used to detect treatment effects using time series data in the presence or absence of intervention. One popular design variant of SCDs is a multiple-baseline design for multiple outcomes, which often collects outcomes with some form of a count. A Poisson model is a natural choice for the count outcome. However, the assumption of the Poisson model that the outcome variable's mean is equal to its variance is often violated in SCDs, as the variance is often larger than the mean (called overdispersion). In addition, when multiple outcomes are from the same participant, it is likely that they are correlated. In this paper, we present a vector Poisson log-normal additive (V-PLN-A) model to deal with (a) change processes (auto- and cross-correlations and data-driven trend) and (b) correlation and overdispersion in multivariate count time series. A multivariate normal distribution was adapted to account for correlation among multiple outcomes as well as possible overdispersion. The V-PLN-A model was applied to an educational intervention study to test treatment effects. Simulation study results showed that parameter recovery of the V-PLN-A model was satisfactory in a large number of timepoints using Bayesian analysis, and that ignoring change processes and overdispersion led to biased estimates of the treatment effects.
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Nuako A, Liu J, Pham G, Smock N, James A, Baker T, Bierut L, Colditz G, Chen LS. Quantifying rural disparity in healthcare utilization in the United States: Analysis of a large midwestern healthcare system. PLoS One 2022; 17:e0263718. [PMID: 35143583 PMCID: PMC8830640 DOI: 10.1371/journal.pone.0263718] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 01/25/2022] [Indexed: 01/03/2023] Open
Abstract
PURPOSE The objective of this study is to identify how predisposing characteristics, enabling factors, and health needs are jointly and individually associated with epidemiological patterns of outpatient healthcare utilization for patients who already interact and engage with a large healthcare system. METHODS We retrospectively analyzed electronic medical record data from 1,423,166 outpatient clinic visits from 474,674 patients in a large healthcare system from June 2018-March 2019. We evaluated patients who exclusively visited rural clinics versus patients who exclusively visited urban clinics using Chi-square tests and the generalized estimating equation Poisson regression methodology. The outcome was healthcare use defined by the number of outpatient visits to clinics within the healthcare system and independent variables included age, gender, race, ethnicity, smoking status, health status, and rural or urban clinic location. Supplementary analyses were conducted observing healthcare use patterns within rural and urban clinics separately and within primary care and specialty clinics separately. FINDINGS Patients in rural clinics vs. urban clinics had worse health status [χ2 = 935.1, df = 3, p<0.0001]. Additionally, patients in rural clinics had lower healthcare utilization than patients in urban clinics, adjusting for age, race, ethnicity, gender, smoking, and health status [2.49 vs. 3.18 visits, RR = 0.61, 95%CI = (0.55,0.68), p<0.0001]. Further, patients in rural clinics had lower utilization for both primary care and specialty care visits. CONCLUSIONS Within the large healthcare system, patients in rural clinics had lower outpatient healthcare utilization compared to their urban counterparts despite having potentially elevated health needs reflected by a higher number of unique health diagnoses documented in their electronic health records after adjusting for multiple factors. This work can inform future studies exploring the roots and ramifications of rural-urban healthcare utilization differences and rural healthcare disparities.
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Affiliation(s)
- Akua Nuako
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States America
| | - Jingxia Liu
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States America
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States America
| | - Giang Pham
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States America
| | - Nina Smock
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States America
| | - Aimee James
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States America
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States America
| | - Timothy Baker
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States America
| | - Laura Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States America
| | - Graham Colditz
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States America
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States America
| | - Li-Shiun Chen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States America
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States America
- * E-mail:
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Soobhug AD, Jowaheer H, Mamode Khan N, Reetoo N, Meethoo-Badulla K, Musango L, Kokonendji CC, Chutoo A, Aries N. Re-analyzing the SARS-CoV-2 series using an extended integer-valued time series models: A situational assessment of the COVID-19 in Mauritius. PLoS One 2022; 17:e0263515. [PMID: 35134059 PMCID: PMC8824322 DOI: 10.1371/journal.pone.0263515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 01/20/2022] [Indexed: 12/20/2022] Open
Abstract
This paper proposes some high-ordered integer-valued auto-regressive time series process of order p (INAR(p)) with Zero-Inflated and Poisson-mixtures innovation distributions, wherein the predictor functions in these mentioned distributions allow for covariate specification, in particular, time-dependent covariates. The proposed time series structures are tested suitable to model the SARs-CoV-2 series in Mauritius which demonstrates excess zeros and hence significant over-dispersion with non-stationary trend. In addition, the INAR models allow the assessment of possible causes of COVID-19 in Mauritius. The results illustrate that the event of Vaccination and COVID-19 Stringency index are the most influential factors that can reduce the locally acquired COVID-19 cases and ultimately, the associated death cases. Moreover, the INAR(7) with Zero-inflated Negative Binomial innovations provides the best fitting and reliable Root Mean Square Errors, based on some short term forecasts. Undeniably, these information will hugely be useful to Mauritian authorities for implementation of comprehensive policies.
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Affiliation(s)
- Ashwinee Devi Soobhug
- Statistics Mauritius, Ministry of Finance, Economic Planning and Development, Port Louis, Mauritius
- * E-mail:
| | - Homeswaree Jowaheer
- Statistics Mauritius, Ministry of Finance, Economic Planning and Development, Port Louis, Mauritius
| | - Naushad Mamode Khan
- Department of Economics and Statistics, University of Mauritius, Moka, Mauritius
| | - Neeshti Reetoo
- Department of Health And Wellness, Ministry of Education, Tertiary Education, Science and Technology, Vacoas-Phoenix, Mauritius
| | | | - Laurent Musango
- World Health Organization Country Representative in Mauritius, Port Louis, Mauritius
| | - Célestin C. Kokonendji
- Laboratoire de Mathématiques de Besançon, UMR 6623 CNRS-UBFC, Université Bourgogne Franche-Comté, Besançon, France
- Department of Mathematics, University of Bangui, Bangui, Central African Republic
| | - Azmi Chutoo
- Department of Economics and Statistics, University of Mauritius, Moka, Mauritius
| | - Nawel Aries
- Faculty of Mathematics, University of Science and Technology Houari Boumediene, Algiers, Bab Ezzouar, Algeria
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Bazirete O, Nzayirambaho M, Umubyeyi A, Karangwa I, Evans M. Risk factors for postpartum haemorrhage in the Northern Province of Rwanda: A case control study. PLoS One 2022; 17:e0263731. [PMID: 35167600 PMCID: PMC8846539 DOI: 10.1371/journal.pone.0263731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 01/26/2022] [Indexed: 12/04/2022] Open
Abstract
Background Postpartum haemorrhage (PPH) remains a major global burden contributing to high maternal mortality and morbidity rates. Assessment of PPH risk factors should be undertaken during antenatal, intrapartum and postpartum periods for timely prevention of maternal morbidity and mortality associated with PPH. The aim of this study is to investigate and model risk factors for primary PPH in Rwanda. Methods We conducted an observational case-control study of 430 (108 cases: 322 controls) pregnant women with gestational age of 32 weeks and above who gave birth in five selected health facilities of Rwanda between January and June 2020. By visual estimation of blood loss, cases of Primary PPH were women who changed the blood-soaked vaginal pads 2 times or more within the first hour after birth, or women requiring a blood transfusion for excessive bleeding after birth. Controls were randomly selected from all deliveries without primary PPH from the same source population. Poisson regression, a generalized linear model with a log link and a Poisson distribution was used to estimate the risk ratio of factors associated with PPH. Results The overall prevalence of primary PPH was 25.2%. Our findings for the following risk factors were: antepartum haemorrhage (RR 3.36, 95% CI 1.80–6.26, P<0.001); multiple pregnancy (RR 1.83; 95% CI 1.11–3.01, P = 0.02) and haemoglobin level <11 gr/dL (RR 1.51, 95% CI 1.00–2.30, P = 0.05). During the intrapartum and immediate postpartum period, the main causes of primary PPH were: uterine atony (RR 6.70, 95% CI 4.78–9.38, P<0.001), retained tissues (RR 4.32, 95% CI 2.87–6.51, P<0.001); and lacerations of genital organs after birth (RR 2.14, 95% CI 1.49–3.09, P<0.001). Coagulopathy was not prevalent in primary PPH. Conclusion Based on our findings, uterine atony remains the foremost cause of primary PPH. As well as other established risk factors for PPH, antepartum haemorrhage and intra uterine fetal death should be included as risk factors in the development and validation of prediction models for PPH. Large scale studies are needed to investigate further potential PPH risk factors.
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Affiliation(s)
- Oliva Bazirete
- College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda
- * E-mail:
| | | | - Aline Umubyeyi
- College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda
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Abstract
BACKGROUND We consider cluster size data of SARS-CoV-2 transmissions for a number of different settings from recently published data. The statistical characteristics of superspreading events are commonly described by fitting a negative binomial distribution to secondary infection and cluster size data as an alternative to the Poisson distribution as it is a longer tailed distribution, with emphasis given to the value of the extra parameter which allows the variance to be greater than the mean. Here we investigate whether other long tailed distributions from more general extended Poisson process modelling can better describe the distribution of cluster sizes for SARS-CoV-2 transmissions. METHODS We use the extended Poisson process modelling (EPPM) approach with nested sets of models that include the Poisson and negative binomial distributions to assess the adequacy of models based on these standard distributions for the data considered. RESULTS We confirm the inadequacy of the Poisson distribution in most cases, and demonstrate the inadequacy of the negative binomial distribution in some cases. CONCLUSIONS The probability of a superspreading event may be underestimated by use of the negative binomial distribution as much larger tail probabilities are indicated by EPPM distributions than negative binomial alternatives. We show that the large shared accommodation, meal and work settings, of the settings considered, have the potential for more severe superspreading events than would be predicted by a negative binomial distribution. Therefore public health efforts to prevent transmission in such settings should be prioritised.
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Affiliation(s)
- M. J. Faddy
- School of Mathematical Sciences and ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, GPO Box 2434, Brisbane, 4001 Australia
| | - A. N. Pettitt
- School of Mathematical Sciences and ARC Centre of Excellence for Mathematical and Statistical Frontiers, QUT, GPO Box 2434, Brisbane, 4001 Australia
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Wang JL, Cao QY, Xin ZJ, Liu SS, Xu M, Wang TG, Lu JL, Chen YH, Wang SY, Zhao ZY, Xu Y, Ning G, Wang WQ, Bi YF, Li M. Association between the Neutrophil-to-lymphocyte Ratio and New-onset Subclinical Macrovascular and Microvascular Diseases in the Chinese Population. Biomed Environ Sci 2022; 35:4-12. [PMID: 35078557 DOI: 10.3967/bes2022.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 12/22/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVE The association between neutrophil-to-lymphocyte ratio (NLR) with subclinical macrovascular and microvascular diseases has been less investigated. We sought to examine the association between NLR and new-onset subclinical macrovascular and microvascular abnormalities in the Chinese population. METHODS From a community cohort, we included 6,430 adults aged ≥ 40 years without subclinical macrovascular and microvascular diseases at baseline. We measured subclinical macrovascular and microvascular abnormalities separately using the ankle-brachial index (ABI), brachial-ankle pulse wave velocity (baPWV), and albuminuria. RESULTS During a mean follow-up of 4.3 years, 110 participants developed incident abnormal ABI, 746 participants developed incident elevated baPWV, and 503 participants developed incident albuminuria. Poisson regression analysis indicated that NLR was significantly associated with an increased risk of new-onset abnormal ABI, elevated baPWV, and albuminuria. Compared to overweight/obese participants, we found a much stronger association between NLR and subclinical vascular abnormalities in participants with normal weight. Furthermore, we found an interaction between the NLR and body mass index (BMI) on the risk of new-onset abnormal ABI ( P for interaction: 0.01). CONCLUSION NLR was associated with subclinical macrovascular and microvascular diseases in the Chinese population. Furthermore, in participants with normal weight, the association between NLR and subclinical vascular abnormalities was much stronger.
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Affiliation(s)
- Jia Lu Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Qiu Yu Cao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zhuo Jun Xin
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shan Shan Liu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Tian Ge Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jie Li Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yu Hong Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shuang Yuan Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zhi Yun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Wei Qing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yu Fang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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Murakami D, Matsui T. Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19. PLoS One 2022; 17:e0260836. [PMID: 34995283 PMCID: PMC8741021 DOI: 10.1371/journal.pone.0260836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 11/17/2021] [Indexed: 11/25/2022] Open
Abstract
In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.
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Affiliation(s)
- Daisuke Murakami
- Department of Statistical Data Science, Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan
- * E-mail:
| | - Tomoko Matsui
- Department of Statistical Modeling, Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan
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Botta-Dukát Z. Devil in the details: how can we avoid potential pitfalls of CATS regression when our data do not follow a Poisson distribution? PeerJ 2022; 10:e12763. [PMID: 35174013 PMCID: PMC8763042 DOI: 10.7717/peerj.12763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 12/17/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Community assembly by trait selection (CATS) allows for the detection of environmental filtering and estimation of the relative role of local and regional (meta-community-level) effects on community composition from trait and abundance data without using environmental data. It has been shown that Poisson regression of abundances against trait data results in the same parameter estimates. Abundance data do not necessarily follow a Poisson distribution, and in these cases, other generalized linear models should be fitted to obtain unbiased parameter estimates. AIMS This paper discusses how the original algorithm for calculating the relative role of local and regional effects has to be modified if Poisson model is not appropriate. RESULTS It can be shown that the use of the logarithm of regional relative abundances as an offset is appropriate only if a log-link function is applied. Otherwise, the link function should be applied to the product of local total abundance and regional relative abundances. Since this product may be outside the domain of the link function, the use of log-link is recommended, even if it is not the canonical link. An algorithm is also suggested for calculating the offset when data are zero-inflated. The relative role of local and regional effects is measured by Kullback-Leibler R2. The formula for this measure presented by Shipley (2014) is valid only if the abundances follow a Poisson distribution. Otherwise, slightly different formulas have to be applied. Beyond theoretical considerations, the proposed refinements are illustrated by numerical examples. CATS regression could be a useful tool for community ecologists, but it has to be slightly modified when abundance data do not follow a Poisson distribution. This paper gives detailed instructions on the necessary refinement.
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Verwijs MH, Haveman-Nies A, Borkent JW, Linschooten JO, Roodenburg AJC, de Groot LCPGM, de van der Schueren MAE. Protein Intake among Community-Dwelling Older Adults: The Influence of (Pre-) Motivational Determinants. Nutrients 2022; 14:nu14020293. [PMID: 35057473 PMCID: PMC8778399 DOI: 10.3390/nu14020293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/04/2022] [Accepted: 01/08/2022] [Indexed: 11/18/2022] Open
Abstract
An adequate protein intake is important for healthy ageing, yet nearly 50% of Dutch community-dwelling older adults do not meet protein recommendations. This study explores protein intake in relation to eight behavioral determinants (I-Change model) among Dutch community-dwelling older adults. Data were collected through an online questionnaire from October 2019–October 2020. Protein intake was assessed by the Protein Screener 55+, indicating a high/low chance of a low protein intake (<1.0 g/kg body weight/day). The behavioral determinants of cognizance, knowledge, risk perception, perceived cues, attitude, social support, self-efficacy and intention were assessed by evaluating statements on a 7-point Likert scale. A total of 824 Dutch community-dwelling older adults were included, recruited via online newsletters, newspapers and by personal approach. Poisson regression was performed to calculate quartile-based prevalence ratios (PRs). Almost 40% of 824 respondents had a high chance of a low protein intake. Univariate analyses indicated that lower scores for all different behavioral determinants were associated with a higher chance of a low protein intake. Independent associations were observed for knowledge (Q4 OR = 0.71) and social support (Q4 OR = 0.71). Results of this study can be used in future interventions aiming to increase protein intake in which focus should lie on increasing knowledge and social support.
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Affiliation(s)
- Marije H. Verwijs
- Department of Nutrition, Dietetics and Lifestyle, School of Allied Health, HAN University of Applied Sciences, Kapittelweg 33, 6525 EN Nijmegen, The Netherlands; (M.H.V.); (J.W.B.)
- Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands;
| | - Annemien Haveman-Nies
- Department of Social Sciences, Consumption and Healthy Lifestyles, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands;
| | - Jos W. Borkent
- Department of Nutrition, Dietetics and Lifestyle, School of Allied Health, HAN University of Applied Sciences, Kapittelweg 33, 6525 EN Nijmegen, The Netherlands; (M.H.V.); (J.W.B.)
- Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands;
| | - Joost O. Linschooten
- Department of Food Science & Technology, HAS University of Applied Sciences, P.O. Box 90108, 5200 MA Den Bosch, The Netherlands; (J.O.L.); (A.J.C.R.)
| | - Annet J. C. Roodenburg
- Department of Food Science & Technology, HAS University of Applied Sciences, P.O. Box 90108, 5200 MA Den Bosch, The Netherlands; (J.O.L.); (A.J.C.R.)
| | - Lisette C. P. G. M. de Groot
- Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands;
| | - Marian A. E. de van der Schueren
- Department of Nutrition, Dietetics and Lifestyle, School of Allied Health, HAN University of Applied Sciences, Kapittelweg 33, 6525 EN Nijmegen, The Netherlands; (M.H.V.); (J.W.B.)
- Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands;
- Correspondence: ; Tel.: +31-6-44296477
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Kasagama E, Todd J, Renju J. Factors associated with changes in adequate antenatal care visits among pregnant women aged 15-49 years in Tanzania from 2004 to 2016. BMC Pregnancy Childbirth 2022; 22:18. [PMID: 34996378 PMCID: PMC8742319 DOI: 10.1186/s12884-021-04350-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 12/18/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Antenatal care (ANC) is crucial for the health of the mother and unborn child as it delivers highly effective health interventions that can prevent maternal and newborn morbidity and mortality. In 2002, the World Health Organization (WHO) recommended a minimum of four ANC visits for a pregnant woman with a positive pregnancy during the entire gestational period. Tanzania has sub-optimal adequate (four or more) ANC visits, and the trend has been fluctuating over time. An understanding of the factors that have been contributing to the fluctuating trend over years is pivotal in increasing the proportions of pregnant women attaining adequate ANC visits in Tanzania. METHODS The study used secondary data from Tanzania Demographic Health Survey (TDHS) from 2004 to 2016. The study included 17976 women aged 15-49 years. Data were analyzed using Stata version 14. Categorical and continuous variables were summarized using descriptive statistics and weighted proportions. A Poisson regression analysis was done to determine factors associated with adequate ANC visits. To determine factors associated with changes in adequate ANC visits among pregnant women in Tanzania from 2004 to 2016, multivariable Poisson decomposition analysis was done. RESULTS The overall proportion of women who had adequate ANC visits in 2004/05, 2010 and 2015/16 was 62, 43 and 51% respectively. The increase in the proportion of women attaining adequate ANC from 2010 to 2015/16 was mainly, 66.2% due to changes in population structure, thus an improvement in health behavior. While 33.8% was due to changes in the mother's characteristics. Early initiation of first ANC visit had contributed 51% of the overall changes in adequate ANC attendance in TDHS 2015/16 survey. CONCLUSION Early ANC initiation has greatly contributed to the increased proportion of pregnant women who attain four or more ANC visits overtime. Interventions on initiating the first ANC visit within the first twelve weeks of pregnancy should be a priority to increase proportion of women with adequate ANC visit.
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Affiliation(s)
- Elizabeth Kasagama
- Department of Epidemiology and Biostatistics, Institute of Public Health, Kilimanjaro Christian Medical University College (KCMUCo), P.O Box 2240, Kilimanjaro, Tanzania
| | - Jim Todd
- London School of Hygiene and Tropical Medicine (LSTM), London, UK
| | - Jenny Renju
- London School of Hygiene and Tropical Medicine (LSTM), London, UK
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Johnson O, Fronterre C, Diggle PJ, Amoah B, Giorgi E. MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists. PLoS One 2022; 16:e0262145. [PMID: 34972193 PMCID: PMC8719748 DOI: 10.1371/journal.pone.0262145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 12/16/2021] [Indexed: 11/19/2022] Open
Abstract
User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for students with only minimal statistical training. In this paper, we illustrate the use of MBGapp for teaching geostatistical analysis to population health scientists. Using a case-study on Loa loa infections, we show how MBGapp can be used to teach the different stages of a geostatistical analysis in a more interactive fashion. For wider accessibility and usability, MBGapp is available as an R package and as a Shiny web-application that can be freely accessed on any web browser. In addition to MBGapp, we also present an auxiliary Shiny app, called VariagramApp, that can be used to aid the teaching of Gaussian processes in one and two dimensions using simulations.
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Affiliation(s)
- Olatunji Johnson
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- * E-mail:
| | - Claudio Fronterre
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Peter J. Diggle
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Benjamin Amoah
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Emanuele Giorgi
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
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