1
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Xiao J, Wang C, Xu G. A Note on Standard Errors for Multidimensional Two-Parameter Logistic Models Using Gaussian Variational Estimation. APPLIED PSYCHOLOGICAL MEASUREMENT 2024; 48:276-294. [PMID: 39166181 PMCID: PMC11331749 DOI: 10.1177/01466216241265757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to be fully addressed. To tackle this issue, the present study proposed an updated supplemented expectation maximization (USEM) method and a bootstrap method for SE estimation. These two methods were compared in terms of SE recovery accuracy. The simulation results demonstrated that the GVEM algorithm with bootstrap and item priors (GVEM-BSP) outperformed the other methods, exhibiting less bias and relative bias for SE estimates under most conditions. Although the GVEM with USEM (GVEM-USEM) was the most computationally efficient method, it yielded an upward bias for SE estimates.
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2
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Melzer AC, Atoma B, Fabbrini AE, Campbell M, Clothier BA, Fu SS. Variation in Reporting of Incidental Findings on Initial Lung Cancer Screening and Associations With Clinician Assessment. J Am Coll Radiol 2024; 21:118-127. [PMID: 37516160 DOI: 10.1016/j.jacr.2023.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/22/2023] [Accepted: 03/27/2023] [Indexed: 07/31/2023]
Abstract
PURPOSE The aim of this study was to quantify the distribution, frequency, and clinical significance of incidental findings (IFs) on initial lung cancer screening (LCS) and the association of report characteristics with subsequent assessment. METHODS Health records of patients undergoing initial LCS from 2015 to 2018 in the Minneapolis VA Health Care System were retrospectively reviewed for demographics, Lung CT Screening Reporting & Data System coding, IFs, and subsequent clinical assessment. IFs were considered potentially significant if they were likely to require any follow-up. High-risk significant IFs (SIFs) were potentially malignant. The primary outcome was the SIF being addressed. Outcomes were analyzed using a mixed-effects model. RESULTS Patients (n = 901) were primarily male (94.1%) smokers (62.1%) with a mean age of 65.2 years. IFs were extremely common (93.9%), with an average of 2.6 IFs per scan (n = 2,296). Seven hundred eighty-six IFs (34.2%) were deemed likely SIFs, of which 58 (7.4%) were high risk. Two hundred twenty-two (28.2%) were addressed by clinicians, of which 104 (13.2%) underwent testing. Reporting of SIFs varied among radiologists, with at least one SIF in the impression in 24% to 78% of low-dose CT studies with the S modifier, used to indicate the presence of a SIF, applied to 0% to 51% of reports. In the mutually adjusted model, radiologist recommendation (adjusted odds ratio [OR], 4.67; 95% confidence interval [CI], 2.23-9.76), high-risk finding (adjusted OR, 4.35; 95% CI, 1.81-10.45), and reporting in the impression (adjusted OR, 2.58; 95% CI, 1.28-5.18) were associated with increased odds of the SIF's being addressed. CONCLUSIONS Radiologists vary in their reporting of IFs on LCS. Further standardization of reporting of SIFs may improve this process, with the simultaneous goals of generating appropriate testing when needed and minimizing low-value care.
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Affiliation(s)
- Anne C Melzer
- Medical Director of Lung Cancer Screening, Center for Care Delivery and Outcomes Research, Minneapolis VA Health Care System, Minneapolis, Minnesota; Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Minnesota Medical School, Minneapolis, Minnesota; Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota.
| | - Bethlehem Atoma
- Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Angela E Fabbrini
- Program Manager, National Center for Lung Cancer Screening, Minneapolis VA Health Care System, Minneapolis, Minnesota
| | - Megan Campbell
- Center for Care Delivery and Outcomes Research, Minneapolis VA Health Care System, Minneapolis, Minnesota
| | - Barbara A Clothier
- Center for Care Delivery and Outcomes Research, Minneapolis VA Health Care System, Minneapolis, Minnesota
| | - Steven S Fu
- Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota; Director, Center for Care Delivery and Outcomes Research, Minneapolis VA Health Care System, Minneapolis, Minnesota
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3
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Sørensen Ø, Fjell AM, Walhovd KB. Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models. PSYCHOMETRIKA 2023; 88:456-486. [PMID: 36976415 PMCID: PMC10188428 DOI: 10.1007/s11336-023-09910-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Indexed: 05/17/2023]
Abstract
We present generalized additive latent and mixed models (GALAMMs) for analysis of clustered data with responses and latent variables depending smoothly on observed variables. A scalable maximum likelihood estimation algorithm is proposed, utilizing the Laplace approximation, sparse matrix computation, and automatic differentiation. Mixed response types, heteroscedasticity, and crossed random effects are naturally incorporated into the framework. The models developed were motivated by applications in cognitive neuroscience, and two case studies are presented. First, we show how GALAMMs can jointly model the complex lifespan trajectories of episodic memory, working memory, and speed/executive function, measured by the California Verbal Learning Test (CVLT), digit span tests, and Stroop tests, respectively. Next, we study the effect of socioeconomic status on brain structure, using data on education and income together with hippocampal volumes estimated by magnetic resonance imaging. By combining semiparametric estimation with latent variable modeling, GALAMMs allow a more realistic representation of how brain and cognition vary across the lifespan, while simultaneously estimating latent traits from measured items. Simulation experiments suggest that model estimates are accurate even with moderate sample sizes.
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Affiliation(s)
| | - Anders M Fjell
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Kristine B Walhovd
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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4
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Fast estimation of multiple group generalized linear latent variable models for categorical observed variables. Comput Stat Data Anal 2023. [DOI: 10.1016/j.csda.2023.107710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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5
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Christoffersen B, Mahjani B, Clements M, Kjellström H, Humphreys K. Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2151454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Benjamin Christoffersen
- Division of Robotics, Perception and Learning, KTH Royal Institute of Technology
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
| | - Behrang Mahjani
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai
| | - Mark Clements
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
| | - Hedvig Kjellström
- Division of Robotics, Perception and Learning, KTH Royal Institute of Technology
- Swedish e-Science Research Center (SeRC)
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Swedish e-Science Research Center (SeRC)
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6
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Li Y, Tahmid IA, Lu F, Bowman DA. Evaluation of Pointing Ray Techniques for Distant Object Referencing in Model-Free Outdoor Collaborative Augmented Reality. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:3896-3906. [PMID: 36048980 DOI: 10.1109/tvcg.2022.3203094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Referencing objects of interest is a common requirement in many collaborative tasks. Nonetheless, accurate object referencing at a distance can be challenging due to the reduced visibility of the objects or the collaborator and limited communication medium. Augmented Reality (AR) may help address the issues by providing virtual pointing rays to the target of common interest. However, such pointing ray techniques can face critical limitations in large outdoor spaces, especially when the environment model is unavailable. In this work, we evaluated two pointing ray techniques for distant object referencing in model-free AR from the literature: the Double Ray technique enhancing visual matching between rays and targets, and the Parallel Bars technique providing artificial orientation cues. Our experiment in outdoor AR involving participants as pointers and observers partially replicated results from a previous study that only evaluated observers in simulated AR. We found that while the effectiveness of the Double Ray technique is reduced with the additional workload for the pointer and human pointing errors, it is still beneficial for distant object referencing.
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7
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The dimension-wise quadrature estimation of dynamic latent variable models for count data. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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8
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Lee HH, Lee JY, Liu SS, Chen CC, Hsu HY. Cryptosporidium parvum infection and management-based risk factors of dairy calves in Taiwan. J Vet Med Sci 2021; 83:1838-1844. [PMID: 34645725 PMCID: PMC8762416 DOI: 10.1292/jvms.21-0366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Cryptosporidiosis is one of the major causes of diarrhea in calves.
Cryptosporidium parvum is considered the most important calf diarrhea
pathogen in the Cryptosporidium species. Not only could infected calves
spread C. parvum, but infected adult cattle could also shed oocysts. The
objectives of this study were (1) to investigate the prevalence of C.
parvum in dairy herds in Taiwan, including calves, the dams in delivery
enclosure, the floor, and the drinking water; (2) to clarify the relationship of diarrhea,
management, and C. parvum infection. Twenty dairy herds in Taiwan were
selected by random sampling, including 226 calves and 198 dams, and other environmental
samples were collected. A questionnaire was filled out by the farm owners to collect
information regarding the management of calves and the delivery enclosure. Hierarchical
logistic regression was used to analyze the risk factors for C. parvum
infection. The prevalence of C. parvum infection in calves was 26.5%
(60/226), while in dams, it was 19.7% (39/198). The C. parvum infection
in calves increased with environmental contamination of C. parvum and
clinical signs of diarrhea, while it decreased with a yard provided in the delivery
enclosure. In conclusion, the management of the delivery enclosure appears to be more
important for preventing cryptosporidiosis in calves in Taiwan.
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Affiliation(s)
- Hsu-Hsun Lee
- Department of Veterinary Medicine, National Pingtung University of Science and Technology.,The Research Center for Animal Biologics, National Pingtung University of Science and Technology
| | - Jui-Yu Lee
- Department of Veterinary Medicine, National Pingtung University of Science and Technology
| | - Shyh-Shyan Liu
- Department of Veterinary Medicine, National Pingtung University of Science and Technology
| | - Chen-Chih Chen
- Department of Veterinary Medicine, National Pingtung University of Science and Technology.,The Research Center for Animal Biologics, National Pingtung University of Science and Technology.,Institute of Wildlife Conservation, National Pingtung University of Science and Technology
| | - Huan-Yu Hsu
- Graduate Institute of Animal Vaccine Technology, National Pingtung University of Science and Technology
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9
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Bianconcini S, Cagnone S. Dynamic latent variable models for the analysis of cognitive abilities in the elderly population. Stat Med 2021; 40:4410-4429. [PMID: 34008240 DOI: 10.1002/sim.9038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 03/10/2021] [Accepted: 03/20/2021] [Indexed: 11/07/2022]
Abstract
Cognitive functioning is a key indicator of overall individual health. Identifying factors related to cognitive status, especially in later life, is of major importance. We concentrate on the analysis of the temporal evolution of cognitive abilities in the elderly population. We propose to model the individual cognitive functioning as a multidimensional latent process that accounts also for the effects of individual-specific characteristics (gender, age, and years of education). The proposed model is specified within the generalized linear latent variable framework, and its efficient estimation is obtained using a recent approximation technique, called dimensionwise quadrature. It provides a fast and streamlined approximate inference for complex models, with better or no degradation in accuracy compared with standard techniques. The methodology is applied to the cognitive assessment data from the Health and Retirement Study combined with the Asset and Health Dynamic study in the years between 2006 and 2010. We evaluate the temporal relationship between two dimensions of cognitive functioning, that is, episodic memory and general mental status. We find a substantial influence of the former on the evolution of the latter, as well as evidence of severe consequences on both cognitive abilities among less-educated and older individuals.
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Affiliation(s)
- Silvia Bianconcini
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
| | - Silvia Cagnone
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
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10
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Dong JJ, Cao J, Gill J, Miles C, Plumb T. Functional joint models for chronic kidney disease in kidney transplant recipients. Stat Methods Med Res 2021; 30:1932-1943. [PMID: 33970050 DOI: 10.1177/09622802211009265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This functional joint model paper is motivated by a chronic kidney disease study post kidney transplantation. The available kidney organ is a scarce resource because millions of end-stage renal patients are on the waiting list for kidney transplantation. The life of the transplanted kidney can be extended if the progression of the chronic kidney disease stage can be slowed, and so a major research question is how to extend the transplanted kidney life to maximize the usage of the scarce organ resource. The glomerular filtration rate is the best test to monitor the progression of the kidney function, and it is a continuous longitudinal outcome with repeated measures. The patient's survival status is characterized by time-to-event outcomes including kidney transplant failure, death with kidney function, and death without kidney function. Few studies have been carried out to simultaneously investigate these multiple clinical outcomes in chronic kidney disease stage patients based on a joint model. Therefore, this paper proposes a new functional joint model from this clinical chronic kidney disease study. The proposed joint models include a longitudinal sub-model with a flexible basis function for subject-level trajectories and a competing-risks sub-model for multiple time-to event outcomes. The different association structures can be accomplished through a time-dependent function of shared random effects from the longitudinal process or the whole longitudinal history in the competing-risks sub-model. The proposed joint model that utilizes basis function and competing-risks sub-model is an extension of the standard linear joint models. The application results from the proposed joint model can supply some useful clinical references for chronic kidney disease study post kidney transplantation.
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Affiliation(s)
- Jianghu James Dong
- Department of Biostatistics, University of Nebraska Medical Center, NE, USA.,Division of Nephrology, University of Nebraska Medical Center, NE, USA
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, Canada
| | - Jagbir Gill
- Department of Medicine, University of British Columbia, BC, Canada
| | - Clifford Miles
- Division of Nephrology, University of Nebraska Medical Center, NE, USA
| | - Troy Plumb
- Division of Nephrology, University of Nebraska Medical Center, NE, USA
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11
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Abstract
Summary
Numerical quadrature methods are needed for many models in order to approximate integrals in the likelihood function. In this note, we correct the error rate given by Liu & Pierce (1994) for integrals approximated with adaptive Gauss–Hermite quadrature and show that the approximation is less accurate than previously thought. We discuss the relationship between the error rates of adaptive Gauss–Hermite quadrature and Laplace approximation, and provide a theoretical explanation of simulation results obtained in previous studies regarding the accuracy of adaptive Gauss–Hermite quadrature.
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Affiliation(s)
- Shaobo Jin
- Department of Statistics, Uppsala University, Box 513, 75120 Uppsala, Sweden
| | - Björn Andersson
- Centre for Educational Measurement, University of Oslo, Box 1161, 0373 Oslo, Norway
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12
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Kabera F, Dufour S, Keefe G, Cameron M, Roy JP. Evaluation of quarter-based selective dry cow therapy using Petrifilm on-farm milk culture: A randomized controlled trial. J Dairy Sci 2020; 103:7276-7287. [PMID: 32505410 DOI: 10.3168/jds.2019-17438] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 04/01/2020] [Indexed: 11/19/2022]
Abstract
The objective of this randomized controlled trial was to assess the efficacy of an on-farm culture system using Petrifilm (3M, London, ON, Canada) for targeted treatment decisions at the quarter level at dry-off and its effects on dry period intramammary infections (IMI) and udder health and milk production in the subsequent lactation. A total of 568 cows (2,247 quarters) from 9 dairy herds with bulk tank somatic cell count <250,000 cells/mL in Québec, Canada, were systematically enrolled and randomly allocated to 4 groups: 2 quarter-based selective (QSDCT) groups, using results of quarter-milk culture on Petrifilm, and 2 blanket dry cow therapy (BDCT) groups. The 2 QSDCT groups consisted of (1) antimicrobial to infected quarters and internal teat sealant (ITS) to healthy quarters (QSDCT/ITS); and (2) antimicrobial and ITS to infected quarters and ITS to healthy quarters (QSDCT+ITS/ITS). The 2 BDCT groups were (1) antimicrobial alone to all quarters (BDCT); and (2) antimicrobial and ITS to all quarters (BDCT+ITS). Quarter milk samples were collected at dry-off and after calving for routine bacteriological culture at the laboratory to monitor IMI; data on milk production, somatic cell count, and clinical mastitis recorded up to 120 d in milk were retrieved from health and DHI records. The probability of avoiding antimicrobial treatment in QSDCT groups was estimated at 48.3% (95% confidence interval: 35.7, 60.9). There was no significant difference between the 4 treatment groups regarding acquisition of new IMI (15.9, 13.2, 15.8, and 15.1% probability for BDCT, BDCT+ITS, QSDCT/ITS, and QSDCT+ITS/ITS, respectively) or persistence of existing IMI (3.2, 2.1, 3.4, and 2.7% probability, respectively) over the dry period. In the subsequent lactation, there was no difference between groups regarding incidence of clinical mastitis (2.4, 3.7, 2.9, and 1.7% respectively for BDCT, BDCT+ITS, QSDCT/ITS, and QSDCT+ITS/ITS), mean milk somatic cell score (1.7, 2.0, 2.0, and 2.0 respectively), or mean daily milk production (43.8, 44.2, 43.2, and 42.6 kg/d, respectively) during the first 120 d in milk. In conclusion, QSDCT using the Petrifilm on-farm culture system to detect infected quarters at dry-off is an interesting option to decrease antibiotic use without any negative effects on udder health or milk production in the first 120 d of the subsequent lactation compared with BDCT.
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Affiliation(s)
- Fidèle Kabera
- Département de pathologie et microbiologie, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, Québec, Canada J2S 2M2; Mastitis Network, Saint-Hyacinthe, Québec, Canada J2S 2M2
| | - Simon Dufour
- Département de pathologie et microbiologie, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, Québec, Canada J2S 2M2; Mastitis Network, Saint-Hyacinthe, Québec, Canada J2S 2M2
| | - Greg Keefe
- Mastitis Network, Saint-Hyacinthe, Québec, Canada J2S 2M2; Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada C1A 4P3
| | - Marguerite Cameron
- Mastitis Network, Saint-Hyacinthe, Québec, Canada J2S 2M2; Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada C1A 4P3
| | - Jean-Philippe Roy
- Mastitis Network, Saint-Hyacinthe, Québec, Canada J2S 2M2; Département de sciences cliniques, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, Québec, Canada J2S 2M2.
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13
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Abstract
Recent methodological studies have investigated the properties of multilevel models with small samples. Previous work has primarily focused on continuous outcomes and little attention has been paid to count outcomes. The estimation of count outcome models can be difficult because the likelihood has no closed-form solution, meaning that approximation methods are required. Although adaptive Gaussian quadrature (AGQ) is generally seen as the gold standard, its comparative performance has been investigated with larger samples. AGQ approximates the full likelihood, a function that is known to produce biased estimates with small samples with continuous outcomes. Conversely, penalized quasi-likelihood (PQL) is considered to be a less desirable approximation; however, it can approximate the restricted likelihood function, a function that is known to perform well with smaller samples with continuous outcomes. The goal of this paper is to compare the small sample bias of full likelihood methods to the linearization bias of PQL with restricted likelihood. Simulation results indicate that the linearization bias of PQL is preferable to the finite sample bias of AGQ with smaller samples.
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14
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Niku J, Brooks W, Herliansyah R, Hui FKC, Taskinen S, Warton DI. Efficient estimation of generalized linear latent variable models. PLoS One 2019; 14:e0216129. [PMID: 31042745 PMCID: PMC6493759 DOI: 10.1371/journal.pone.0216129] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 04/15/2019] [Indexed: 01/13/2023] Open
Abstract
Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species are collected from a set of sites. Until very recently, the main challenge in fitting GLLVMs has been the lack of computationally efficient estimation methods. For likelihood based estimation, several closed form approximations for the marginal likelihood of GLLVMs have been proposed, but their efficient implementations have been lacking in the literature. To fill this gap, we show in this paper how to obtain computationally convenient estimation algorithms based on a combination of either the Laplace approximation method or variational approximation method, and automatic optimization techniques implemented in R software. An extensive set of simulation studies is used to assess the performances of different methods, from which it is shown that the variational approximation method used in conjunction with automatic optimization offers a powerful tool for estimation.
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Affiliation(s)
- Jenni Niku
- Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland
| | - Wesley Brooks
- School of Mathematics and Statistics, The University of New South Wales, Sydney, Australia
| | - Riki Herliansyah
- Department of Mathematics, Kalimantan Institute of Technology, Kalimantan, Indonesia
| | - Francis K. C. Hui
- Research School of Finance, Actuarial Studies & Statistics, Australian National University, Canberra, Australia
| | - Sara Taskinen
- Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland
| | - David I. Warton
- School of Mathematics and Statistics, The University of New South Wales, Sydney, Australia
- Evolution & Ecology Research Centre, The University of New South Wales, Sydney, Australia
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15
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Noh M, Lee Y, Oud JH, Toharudin T. Hierarchical likelihood approach to non-Gaussian factor analysis. J STAT COMPUT SIM 2019. [DOI: 10.1080/00949655.2019.1590575] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Maengseok Noh
- Department of Statistics, Pukyong National University, Busan, South Korea
| | - Youngjo Lee
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Johan H.L. Oud
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Toni Toharudin
- Statistic Department, Padjadjaran University, Bandang, Indonesia
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16
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Shear BR. Using Hierarchical Logistic Regression to Study DIF and DIF Variance in Multilevel Data. JOURNAL OF EDUCATIONAL MEASUREMENT 2018. [DOI: 10.1111/jedm.12190] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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17
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Zhou G, Wu L. Modeling semicontinuous longitudinal data with order constraints. Stat Med 2018; 37:4758-4770. [DOI: 10.1002/sim.7963] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 08/12/2018] [Accepted: 08/21/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Guohai Zhou
- Department of Statistics; University of British Columbia; Vancouver BC Canada
| | - Lang Wu
- Department of Statistics; University of British Columbia; Vancouver BC Canada
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18
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Cho SJ, Brown-Schmidt S, Lee WY. Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data. PSYCHOMETRIKA 2018; 83:751-771. [PMID: 29417454 DOI: 10.1007/s11336-018-9604-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 11/21/2017] [Indexed: 05/28/2023]
Abstract
As a method to ascertain person and item effects in psycholinguistics, a generalized linear mixed effect model (GLMM) with crossed random effects has met limitations in handing serial dependence across persons and items. This paper presents an autoregressive GLMM with crossed random effects that accounts for variability in lag effects across persons and items. The model is shown to be applicable to intensive binary time series eye-tracking data when researchers are interested in detecting experimental condition effects while controlling for previous responses. In addition, a simulation study shows that ignoring lag effects can lead to biased estimates and underestimated standard errors for the experimental condition effects.
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19
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Giorgi E, Diggle PJ, Snow RW, Noor AM. Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio-temporally Referenced Prevalence Surveys. Int Stat Rev 2018; 86:571-597. [PMID: 33184527 DOI: 10.1111/insr.12268] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio-temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram-based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood-based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio-temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio-temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014.
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Affiliation(s)
- Emanuele Giorgi
- Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Peter J Diggle
- Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Robert W Snow
- Population and Health Theme, Kenya Medical Research Institute - Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Abdisalan M Noor
- Population and Health Theme, Kenya Medical Research Institute - Wellcome Trust Research Programme, Nairobi, Kenya
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20
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Chernyavskiy P, Little MP, Rosenberg PS. Correlated Poisson models for age-period-cohort analysis. Stat Med 2018; 37:405-424. [PMID: 28980325 PMCID: PMC5768446 DOI: 10.1002/sim.7519] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2016] [Revised: 08/22/2017] [Accepted: 09/11/2017] [Indexed: 11/06/2022]
Abstract
Age-period-cohort (APC) models are widely used to analyze population-level rates, particularly cancer incidence and mortality. These models are used for descriptive epidemiology, comparative risk analysis, and extrapolating future disease burden. Traditional APC models have 2 major limitations: (1) they lack parsimony because they require estimation of deviations from linear trends for each level of age, period, and cohort; and (2) rates observed at similar ages, periods, and cohorts are treated as independent, ignoring any correlations between them that may lead to biased parameter estimates and inefficient standard errors. We propose a novel approach to estimation of APC models using a spatially correlated Poisson model that accounts for over-dispersion and correlations in age, period, and cohort, simultaneously. We treat the outcome of interest as event rates occurring over a grid defined by values of age, period, and cohort. Rates defined in this manner lend themselves to well-established approaches from spatial statistics in which correlation among proximate observations may be modeled using a spatial random effect. Through simulations, we show that in the presence of spatial dependence and over-dispersion: (1) the correlated Poisson model attains lower AIC; (2) the traditional APC model produces biased trend parameter estimates; and (3) the correlated Poisson model corrects most of this bias. We illustrate our approach using brain and breast cancer incidence rates from the Surveillance Epidemiology and End Results Program of the United States. Our approach can be easily extended to accommodate comparative risk analyses and interpolation of cells in the Lexis with sparse data.
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Affiliation(s)
- Pavel Chernyavskiy
- DHHS, NIH, Division of Cancer Epidemiology and Genetics, Radiation Epidemiology Branch, National Cancer Institute, Bethesda, MD, 20892-9778, USA
- DHHS, NIH, Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, Bethesda, MD, 20892-9778, USA
| | - Mark P Little
- DHHS, NIH, Division of Cancer Epidemiology and Genetics, Radiation Epidemiology Branch, National Cancer Institute, Bethesda, MD, 20892-9778, USA
| | - Philip S Rosenberg
- DHHS, NIH, Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, Bethesda, MD, 20892-9778, USA
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21
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Jeon M, Kaufman C, Rabe-Hesketh S. Monte Carlo local likelihood approximation. Biostatistics 2018; 20:164-179. [DOI: 10.1093/biostatistics/kxx070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 12/10/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Minjeong Jeon
- University of California, Los Angeles, CA 90024, USA
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22
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Rajeswaran J, Blackstone EH, Ehrlinger J, Li L, Ishwaran H, Parides MK. Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model. Stat Methods Med Res 2018; 27:126-141. [PMID: 26740575 PMCID: PMC5633490 DOI: 10.1177/0962280215623583] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of associated risk factors. A generalized nonlinear mixed effects model is presented to estimate the time-related probability of atrial fibrillation using a temporal decomposition approach to reveal the pattern of the probability of atrial fibrillation and their determinants. This methodology generalizes to patient-specific analysis of longitudinal binary data with possibly time-varying effects of covariates and with different patient-specific random effects influencing different temporal phases. The motivation and application of this model is illustrated using longitudinally measured atrial fibrillation data obtained through weekly trans-telephonic monitoring from an NIH sponsored clinical trial being conducted by the Cardiothoracic Surgery Clinical Trials Network.
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Affiliation(s)
| | | | - John Ehrlinger
- Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio
| | - Liang Li
- The University of Texas MD Anderson Cancer Center, University of Texas, Houston, Texas
| | - Hemant Ishwaran
- Division of Biostatistics, University of Miami, Miami, Florida
| | - Michael K. Parides
- Mount Sinai Center for Biostatistics, Mount Sinai Hospital, New York, New York
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23
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Zhang H, Wong H, Wu L. A mechanistic nonlinear model for censored and mismeasured covariates in longitudinal models, with application in AIDS studies. Stat Med 2017; 37:167-178. [PMID: 29034494 DOI: 10.1002/sim.7515] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2016] [Revised: 05/17/2017] [Accepted: 09/05/2017] [Indexed: 11/09/2022]
Abstract
When modeling longitudinal data, the true values of time-varying covariates may be unknown because of detection-limit censoring or measurement error. A common approach in the literature is to empirically model the covariate process based on observed data and then predict the censored values or mismeasured values based on this empirical model. Such an empirical model can be misleading, especially for censored values since the (unobserved) censored values may behave very differently than observed values due to the underlying data-generation mechanisms or disease status. In this paper, we propose a mechanistic nonlinear covariate model based on the underlying data-generation mechanisms to address censored values and mismeasured values. Such a mechanistic model is based on solid scientific or biological arguments, so the predicted censored or mismeasured values are more reasonable. We use a Monte Carlo EM algorithm for likelihood inference and apply the methods to an AIDS dataset, where viral load is censored by a lower detection limit. Simulation results confirm that the proposed models and methods offer substantial advantages over existing empirical covariate models for censored and mismeasured covariates.
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Affiliation(s)
- Hongbin Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, City University of New York, 55 West 125th Street, New York, NY 10027, USA
| | - Hubert Wong
- CIHR Canadian HIV Trials Network, St. Paul's Hospital, Vancouver, BC V6Z 1Y6, Canada
| | - Lang Wu
- Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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24
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Grilli L, Innocenti F. Fitting logistic multilevel models with crossed random effects via Bayesian Integrated Nested Laplace Approximations: a simulation study. J STAT COMPUT SIM 2017. [DOI: 10.1080/00949655.2017.1341886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Leonardo Grilli
- Dipartimento di Statistica, Informatica, Applicazioni ‘G. Parenti’, Università di Firenze, Firenze, Italy
| | - Francesco Innocenti
- Department of Methodology and Statistics, Maastricht University, Maastricht, Netherlands
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25
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Cho SJ, De Boeck P, Lee WY. Evaluating Testing, Profile Likelihood Confidence Interval Estimation, and Model Comparisons for Item Covariate Effects in Linear Logistic Test Models. APPLIED PSYCHOLOGICAL MEASUREMENT 2017; 41:353-371. [PMID: 29881097 PMCID: PMC5978674 DOI: 10.1177/0146621617692078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The linear logistic test model (LLTM) has been widely applied to investigate the effects of item covariates on item difficulty. The LLTM was extended with random item residuals to account for item differences not explained by the item covariates. This extended LLTM is called the LLTM-R. In this article, statistical inference methods are investigated for these two models. Type I error rates and power are compared via Monte Carlo studies. Based on the simulation results, the use of the likelihood ratio test (LRT) is recommended over the paired-sample t test based on sum scores, the Wald z test, and information criteria, and the LRT is recommended over the profile likelihood confidence interval because of the simplicity of the LRT. In addition, it is concluded that the LLTM-R is the better general model approach. Inferences based on the LLTM while the LLTM-R is the true model appear to be largely biased in the liberal way, while inferences based on the LLTM-R while the LLTM is the true model are only biased in a very minor and conservative way. Furthermore, in the absence of residual variance, Type I error rate and power were acceptable except for power when the number of items is small (10 items) and also the number of persons is small (200 persons). In the presence of residual variance, however, the number of items needs to be large (80 items) to avoid an inflated Type I error and to reach a power level of .90 for a moderate effect.
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26
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Chernyavskiy P, Little MP, Rosenberg PS. A unified approach for assessing heterogeneity in age-period-cohort model parameters using random effects. Stat Methods Med Res 2017; 28:20-34. [PMID: 28589750 DOI: 10.1177/0962280217713033] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Age-period-cohort models are a popular tool for studying population-level rates; for example, trends in cancer incidence and mortality. Age-period-cohort models decompose observed trends into age effects that correlate with natural history, period effects that reveal factors impacting all ages simultaneously (e.g. innovations in screening), and birth cohort effects that reflect differential risk exposures that vary across birth years. Methodology for the analysis of multiple population strata (e.g. ethnicity, cancer registry) within the age-period-cohort framework has not been thoroughly investigated. Here, we outline a general model for characterizing differences in age-period-cohort model parameters for a potentially large number of strata. Our model incorporates stratum-specific random effects for the intercept, the longitudinal age trend, and the model-based estimate of annual percent change (net drift), thereby enabling a comprehensive analysis of heterogeneity. We also extend the standard model to include quadratic terms for age, period, and cohort, along with the corresponding random effects, which quantify possible stratum-specific departures from global curvature. We illustrate the utility of our model with an application to metastatic prostate cancer incidence (2004-2013) in non-Hispanic white and black men, using 17 population-based cancer registries in the Surveillance, Epidemiology, and End Results Program.
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Affiliation(s)
- Pavel Chernyavskiy
- 1 Radiation Epidemiology Branch, National Cancer Institute, USA.,2 Biostatistics Branch, National Cancer Institute, USA
| | - Mark P Little
- 1 Radiation Epidemiology Branch, National Cancer Institute, USA
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27
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Jeon M, Rijmen F, Rabe-Hesketh S. A Variational Maximization-Maximization Algorithm for Generalized Linear Mixed Models with Crossed Random Effects. PSYCHOMETRIKA 2017; 82:10.1007/s11336-017-9555-z. [PMID: 28247165 DOI: 10.1007/s11336-017-9555-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 10/18/2016] [Indexed: 06/06/2023]
Abstract
We present a variational maximization-maximization algorithm for approximate maximum likelihood estimation of generalized linear mixed models with crossed random effects (e.g., item response models with random items, random raters, or random occasion-specific effects). The method is based on a factorized variational approximation of the latent variable distribution given observed variables, which creates a lower bound of the log marginal likelihood. The lower bound is maximized with respect to the factorized distributions as well as model parameters. With the proposed algorithm, a high-dimensional intractable integration is translated into a two-dimensional integration problem. We incorporate an adaptive Gauss-Hermite quadrature method in conjunction with the variational method in order to increase computational efficiency. Numerical studies show that under the small sample size conditions that are considered the proposed algorithm outperforms the Laplace approximation.
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Affiliation(s)
- Minjeong Jeon
- Department of Education, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA, 90095 , USA.
| | - Frank Rijmen
- American Institutes for Research, Washington D.C., USA
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28
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Bianconcini S, Cagnone S, Rizopoulos D. Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature. Electron J Stat 2017. [DOI: 10.1214/17-ejs1360] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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29
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Rupšys P. New Insights into Tree Height Distribution Based on Mixed Effects Univariate Diffusion Processes. PLoS One 2016; 11:e0168507. [PMID: 28002500 PMCID: PMC5176318 DOI: 10.1371/journal.pone.0168507] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 12/01/2016] [Indexed: 11/18/2022] Open
Abstract
The aim of this paper is twofold: to introduce the mathematics of stochastic differential equations (SDEs) for forest dynamics modeling and to describe how such a model can be applied to aid our understanding of tree height distribution corresponding to a given diameter using the large dataset provided by the Lithuanian National Forest Inventory (LNFI). Tree height-diameter dynamics was examined with Ornstein-Uhlenbeck family mixed effects SDEs. Dynamics of a tree height, volume and their coefficients of variation, quantile regression curves of the tree height, and height-diameter ratio were demonstrated using newly developed tree height distributions for a given diameter. The parameters were estimated by considering a discrete sample of the diameter and height and by using an approximated maximum likelihood procedure. All models were evaluated using a validation dataset. The dataset provided by the LNFI (2006-2010) of Scots pine trees is used in this study to estimate parameters and validate our modeling technique. The verification indicated that the newly developed models are able to accurately capture the behavior of tree height distribution corresponding to a given diameter. All of the results were implemented in a MAPLE symbolic algebra system.
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Affiliation(s)
- Petras Rupšys
- Centre of Mathematics, Physics and Information Technologies, Aleksandras Stulginskis University, Kaunas, Lithuania
- Institute of Forest Management and Wood Sciences, Aleksandras Stulginskis University, Kaunas, Lithuania
- * E-mail:
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30
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McNeish D. Estimation Methods for Mixed Logistic Models with Few Clusters. MULTIVARIATE BEHAVIORAL RESEARCH 2016; 51:790-804. [PMID: 27802068 DOI: 10.1080/00273171.2016.1236237] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
For mixed models generally, it is well known that modeling data with few clusters will result in biased estimates, particularly of the variance components and fixed effect standard errors. In linear mixed models, small sample bias is typically addressed through restricted maximum likelihood estimation (REML) and a Kenward-Roger correction. Yet with binary outcomes, there is no direct analog of either procedure. With a larger number of clusters, estimation methods for binary outcomes that approximate the likelihood to circumvent the lack of a closed form solution such as adaptive Gaussian quadrature and the Laplace approximation have been shown to yield less-biased estimates than linearization estimation methods that instead linearly approximate the model. However, adaptive Gaussian quadrature and the Laplace approximation are approximating the full likelihood rather than the restricted likelihood; the full likelihood is known to yield biased estimates with few clusters. On the other hand, linearization methods linearly approximate the model, which allows for restricted maximum likelihood and the Kenward-Roger correction to be applied. Thus, the following question arises: Which is preferable, a better approximation of a biased function or a worse approximation of an unbiased function? We address this question with a simulation and an illustrative empirical analysis.
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Affiliation(s)
- Daniel McNeish
- a Utrecht University ; University of Maryland , College Park
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31
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Diggle PJ, Giorgi E. Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings. J Am Stat Assoc 2016. [DOI: 10.1080/01621459.2015.1123158] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Peter J. Diggle
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Emanuele Giorgi
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
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32
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Zhou G, Wu L, Brant R, Ansermino JM. A likelihood-based approach for multivariate one-sided tests with missing data. J Appl Stat 2016. [DOI: 10.1080/02664763.2016.1238054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Guohai Zhou
- Department of Statistics, University of British Columbia, Vancouver, Canada
| | - Lang Wu
- Department of Statistics, University of British Columbia, Vancouver, Canada
| | - Rollin Brant
- Department of Statistics, University of British Columbia, Vancouver, Canada
| | - J. Mark Ansermino
- Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, Canada
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33
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Rajeswaran J, Blackstone EH. A multiphase non-linear mixed effects model: An application to spirometry after lung transplantation. Stat Methods Med Res 2016; 26:21-42. [PMID: 24919830 DOI: 10.1177/0962280214537255] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time-varying coefficients.
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Affiliation(s)
- Jeevanantham Rajeswaran
- Department of Quantitative Health Sciences, Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA
| | - Eugene H Blackstone
- Department of Quantitative Health Sciences, Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA
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34
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De la Cruz R, Meza C, Arribas-Gil A, Carroll RJ. Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements. J MULTIVARIATE ANAL 2016; 143:94-106. [PMID: 27274601 DOI: 10.1016/j.jmva.2015.08.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification.
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Affiliation(s)
- Rolando De la Cruz
- Institute of Statistics, Pontificia Universidad Católica de Valparaíso, Av. Errazuriz 2734, Valparaíso, Chile
| | - Cristian Meza
- Centro de Investigación y Modelamiento de Fenómenos Aleatorios-CIMFAV, Faculty of Engineering, Universidad de Valparaíso, General Cruz 222, Valparaíso, Chile
| | - Ana Arribas-Gil
- Departamento de Estadística, Universidad Carlos III de Madrid, Getafe, Spain
| | - Raymond J Carroll
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
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35
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Nikoloulopoulos AK. A mixed effect model for bivariate meta-analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution. Stat Med 2015; 34:3842-65. [PMID: 26234584 DOI: 10.1002/sim.6595] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2014] [Revised: 05/01/2015] [Accepted: 07/04/2015] [Indexed: 11/07/2022]
Abstract
Diagnostic test accuracy studies typically report the number of true positives, false positives, true negatives and false negatives. There usually exists a negative association between the number of true positives and true negatives, because studies that adopt less stringent criterion for declaring a test positive invoke higher sensitivities and lower specificities. A generalized linear mixed model (GLMM) is currently recommended to synthesize diagnostic test accuracy studies. We propose a copula mixed model for bivariate meta-analysis of diagnostic test accuracy studies. Our general model includes the GLMM as a special case and can also operate on the original scale of sensitivity and specificity. Summary receiver operating characteristic curves are deduced for the proposed model through quantile regression techniques and different characterizations of the bivariate random effects distribution. Our general methodology is demonstrated with an extensive simulation study and illustrated by re-analysing the data of two published meta-analyses. Our study suggests that there can be an improvement on GLMM in fit to data and makes the argument for moving to copula random effects models. Our modelling framework is implemented in the package CopulaREMADA within the open source statistical environment R.
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36
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Chalmers RP. Extended Mixed-Effects Item Response Models With the MH-RM Algorithm. JOURNAL OF EDUCATIONAL MEASUREMENT 2015. [DOI: 10.1111/jedm.12072] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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37
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Lanini S, Costa AN, Puro V, Procaccio F, Grossi PA, Vespasiano F, Ricci A, Vesconi S, Ison MG, Carmeli Y, Ippolito G. Incidence of carbapenem-resistant gram negatives in Italian transplant recipients: a nationwide surveillance study. PLoS One 2015; 10:e0123706. [PMID: 25835018 PMCID: PMC4383484 DOI: 10.1371/journal.pone.0123706] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Accepted: 03/06/2015] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Bacterial infections remain a challenge to solid organ transplantation. Due to the alarming spread of carbapenem-resistant gram negative bacteria, these organisms have been frequently recognized as cause of severe infections in solid organ transplant recipients. METHODS AND FINDINGS Between 15 May and 30 September 2012 we enrolled 887 solid organ transplant recipients in Italy with the aim to describe the epidemiology of gram negative bacteria spreading, to explore potential risk factors and to assess the effect of early isolation of gram negative bacteria on recipients' mortality during the first 90 days after transplantation. During the study period 185 clinical isolates of gram negative bacteria were reported, for an incidence of 2.39 per 1000 recipient-days. Positive cultures for gram negative bacteria occurred early after transplantation (median time 26 days; incidence rate 4.33, 1.67 and 1.14 per 1,000 recipient-days in the first, second and third month after SOT, respectively). Forty-nine of these clinical isolates were due to carbapenem-resistant gram negative bacteria (26.5%; incidence 0.63 per 1000 recipient-days). Carbapenems resistance was particularly frequent among Klebsiella spp. isolates (49.1%). Recipients with longer hospital stay and those who received either heart or lung graft were at the highest risk of testing positive for any gram negative bacteria. Moreover recipients with longer hospital stay, lung recipients and those admitted to hospital for more than 48h before transplantation had the highest probability to have culture(s) positive for carbapenem-resistant gram negative bacteria. Forty-four organ recipients died (0.57 per 1000 recipient-days) during the study period. Recipients with at least one positive culture for carbapenem-resistant gram negative bacteria had a 10.23-fold higher mortality rate than those who did not. CONCLUSION The isolation of gram-negative bacteria is most frequent among recipient with hospital stays >48 hours prior to transplant and in those receiving either heart or lung transplants. Carbapenem-resistant gram negative isolates are associated with significant mortality.
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Affiliation(s)
- Simone Lanini
- Department of Epidemiology and Preclinical Research, National Institute for Infectious Diseases “Lazzaro Spallanzani”, Rome, Italy
| | - Alessandro Nanni Costa
- Italian National Transplant Center, Italian National Institute of Health (ISS), Rome, Italy
| | - Vincenzo Puro
- Department of Epidemiology and Preclinical Research, National Institute for Infectious Diseases “Lazzaro Spallanzani”, Rome, Italy
| | - Francesco Procaccio
- Italian National Transplant Center, Italian National Institute of Health (ISS), Rome, Italy
| | - Paolo Antonio Grossi
- Division of Infectious Diseases, Department of Surgical and Morphological Sciences, University of Insubria-Ospedale di Circolo, Varese, Italy
| | - Francesca Vespasiano
- Italian National Transplant Center, Italian National Institute of Health (ISS), Rome, Italy
| | - Andrea Ricci
- Italian National Transplant Center, Italian National Institute of Health (ISS), Rome, Italy
| | - Sergio Vesconi
- Direzione Generale Salute Lombardia, Regione Lombardia, Milan, Italy
| | - Michael G. Ison
- Divisions of Infectious Diseases & Organ Transplantation, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Yehuda Carmeli
- Division of Epidemiology and the Laboratory for Molecular Epidemiology and Antibiotic Research, Sourasky Medical Center, Tel Aviv, Israel
| | - Giuseppe Ippolito
- Department of Epidemiology and Preclinical Research, National Institute for Infectious Diseases “Lazzaro Spallanzani”, Rome, Italy
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Elghafghuf A, Dufour S, Reyher K, Dohoo I, Stryhn H. Survival analysis of clinical mastitis data using a nested frailty Cox model fit as a mixed-effects Poisson model. Prev Vet Med 2014; 117:456-68. [PMID: 25449735 DOI: 10.1016/j.prevetmed.2014.09.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Revised: 05/15/2014] [Accepted: 09/30/2014] [Indexed: 11/26/2022]
Abstract
Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation approach for frailty Cox models based on the penalized partial likelihood. The simulation study showed good performance for the Poisson maximum likelihood approach with Gaussian quadrature and biased variance component estimates for both the Poisson maximum likelihood with Laplace approximation and penalized partial likelihood approaches.
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Affiliation(s)
- Adel Elghafghuf
- Centre for Veterinary Epidemiological Research, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada; Department of Statistics, Faculty of Science, University of Misurata, P.O. Box 2478, Misurata, Libya.
| | - Simon Dufour
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, University of Montreal, C.P. 5000, St-Hyacinthe, Quebec J2S 7C6, Canada
| | - Kristen Reyher
- Farm Animal Science, School of Veterinary Sciences, University of Bristol, Langford BS40 5DU, UK
| | - Ian Dohoo
- Centre for Veterinary Epidemiological Research, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada
| | - Henrik Stryhn
- Centre for Veterinary Epidemiological Research, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada
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39
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Bianconcini S. Asymptotic properties of adaptive maximum likelihood estimators in latent variable models. BERNOULLI 2014. [DOI: 10.3150/13-bej531] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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40
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Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2013.11.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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41
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Schelldorfer J, Meier L, Bühlmann P. GLMMLasso: An Algorithm for High-Dimensional Generalized Linear Mixed Models Using ℓ1-Penalization. J Comput Graph Stat 2014. [DOI: 10.1080/10618600.2013.773239] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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42
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Bianconcini S, Cagnone S. The Role of Posterior Densities in Latent Variable Models for Ordinal Data. COMMUN STAT-THEOR M 2014. [DOI: 10.1080/03610926.2013.810266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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43
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Cho SJ, De Boeck P, Embretson S, Rabe-Hesketh S. Additive multilevel item structure models with random residuals: item modeling for explanation and item generation. PSYCHOMETRIKA 2014; 79:84-104. [PMID: 24337937 DOI: 10.1007/s11336-013-9360-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2011] [Indexed: 06/03/2023]
Abstract
An additive multilevel item structure (AMIS) model with random residuals is proposed. The model includes multilevel latent regressions of item discrimination and item difficulty parameters on covariates at both item and item category levels with random residuals at both levels. The AMIS model is useful for explanation purposes and also for prediction purposes as in an item generation context. The parameters can be estimated with an alternating imputation posterior algorithm that makes use of adaptive quadrature, and the performance of this algorithm is evaluated in a simulation study.
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44
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Nikoloulopoulos AK. On the estimation of normal copula discrete regression models using the continuous extension and simulated likelihood. J Stat Plan Inference 2013. [DOI: 10.1016/j.jspi.2013.06.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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45
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Kim Y, Choi YK, Emery S. Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages. AM STAT 2013; 67. [PMID: 24288415 DOI: 10.1080/00031305.2013.817357] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.
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Affiliation(s)
- Yoonsang Kim
- Institute for Health Research and Policy, University of Illinois at Chicago, 1747 W. Roosevelt Rd., Chicago, IL 60608
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46
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Capanu M, Gönen M, Begg CB. An assessment of estimation methods for generalized linear mixed models with binary outcomes. Stat Med 2013; 32:4550-66. [PMID: 23839712 DOI: 10.1002/sim.5866] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2012] [Accepted: 05/08/2013] [Indexed: 02/02/2023]
Abstract
Two main classes of methodology have been developed for addressing the analytical intractability of generalized linear mixed models: likelihood-based methods and Bayesian methods. Likelihood-based methods such as the penalized quasi-likelihood approach have been shown to produce biased estimates especially for binary clustered data with small clusters sizes. More recent methods using adaptive Gaussian quadrature perform well but can be overwhelmed by problems with large numbers of random effects, and efficient algorithms to better handle these situations have not yet been integrated in standard statistical packages. Bayesian methods, although they have good frequentist properties when the model is correct, are known to be computationally intensive and also require specialized code, limiting their use in practice. In this article, we introduce a modification of the hybrid approach of Capanu and Begg, 2011, Biometrics 67, 371-380, as a bridge between the likelihood-based and Bayesian approaches by employing Bayesian estimation for the variance components followed by Laplacian estimation for the regression coefficients. We investigate its performance as well as that of several likelihood-based methods in the setting of generalized linear mixed models with binary outcomes. We apply the methods to three datasets and conduct simulations to illustrate their properties. Simulation results indicate that for moderate to large numbers of observations per random effect, adaptive Gaussian quadrature and the Laplacian approximation are very accurate, with adaptive Gaussian quadrature preferable as the number of observations per random effect increases. The hybrid approach is overall similar to the Laplace method, and it can be superior for data with very sparse random effects.
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Affiliation(s)
- Marinela Capanu
- Memorial Sloan-Kettering Cancer Center, 307 E 63rd St, 3rd Floor, New York, NY 10021, U.S.A
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47
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Bouwmeester W, Moons KGM, Kappen TH, van Klei WA, Twisk JWR, Eijkemans MJC, Vergouwe Y. Internal validation of risk models in clustered data: a comparison of bootstrap schemes. Am J Epidemiol 2013; 177:1209-17. [PMID: 23660796 DOI: 10.1093/aje/kws396] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Internal validity of a risk model can be studied efficiently with bootstrapping to assess possible optimism in model performance. Assumptions of the regular bootstrap are violated when the development data are clustered. We compared alternative resampling schemes in clustered data for the estimation of optimism in model performance. A simulation study was conducted to compare regular resampling on only the patient level with resampling on only the cluster level and with resampling sequentially on both the cluster and patient levels (2-step approach). Optimism for the concordance index and calibration slope was estimated. Resampling of only patients or only clusters showed accurate estimates of optimism in model performance. The 2-step approach overestimated the optimism in model performance. If the number of centers or intraclass correlation coefficient was high, resampling of clusters showed more accurate estimates than resampling of patients. The 3 bootstrap schemes also were applied to empirical data that were clustered. The results presented in this paper support the use of resampling on only the clusters for estimation of optimism in model performance when data are clustered.
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Affiliation(s)
- W Bouwmeester
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
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Cho SJ, Partchev I, De Boeck P. Parameter estimation of multiple item response profile model. THE BRITISH JOURNAL OF MATHEMATICAL AND STATISTICAL PSYCHOLOGY 2012; 65:438-466. [PMID: 22070786 DOI: 10.1111/j.2044-8317.2011.02036.x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Multiple item response profile (MIRP) models are models with crossed fixed and random effects. At least one between-person factor is crossed with at least one within-person factor, and the persons nested within the levels of the between-person factor are crossed with the items within levels of the within-person factor. Maximum likelihood estimation (MLE) of models for binary data with crossed random effects is challenging. This is because the marginal likelihood does not have a closed form, so that MLE requires numerical or Monte Carlo integration. In addition, the multidimensional structure of MIRPs makes the estimation complex. In this paper, three different estimation methods to meet these challenges are described: the Laplace approximation to the integrand; hierarchical Bayesian analysis, a simulation-based method; and an alternating imputation posterior with adaptive quadrature as the approximation to the integral. In addition, this paper discusses the advantages and disadvantages of these three estimation methods for MIRPs. The three algorithms are compared in a real data application and a simulation study was also done to compare their behaviour.
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Affiliation(s)
- Sun-Joo Cho
- College of Vanderbilt University, Nashville, Tennessee 37203-5721, USA.
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49
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Bianconcini S, Cagnone S. Estimation of generalized linear latent variable models via fully exponential Laplace approximation. J MULTIVARIATE ANAL 2012. [DOI: 10.1016/j.jmva.2012.06.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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50
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An investigation of the nature of the influences of item stem and option representation on student responses to a mathematics test. EUROPEAN JOURNAL OF PSYCHOLOGY OF EDUCATION 2012. [DOI: 10.1007/s10212-012-0159-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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