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Keller BT, Enders CK. An Investigation of Factored Regression Missing Data Methods for Multilevel Models with Cross-Level Interactions. MULTIVARIATE BEHAVIORAL RESEARCH 2023; 58:938-963. [PMID: 36602079 DOI: 10.1080/00273171.2022.2147049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
A growing body of literature has focused on missing data methods that factorize the joint distribution into a part representing the analysis model of interest and a part representing the distributions of the incomplete predictors. Relatively little is known about the utility of this method for multilevel models with interactive effects. This study presents a series of Monte Carlo computer simulations that investigates Bayesian and multiple imputation strategies based on factored regressions. When the model's distributional assumptions are satisfied, these methods generally produce nearly unbiased estimates and good coverage, with few exceptions. Severe misspecifications that arise from substantially non-normal distributions can introduce biased estimates and poor coverage. Follow-up simulations suggest that a Yeo-Johnson transformation can mitigate these biases. A real data example illustrates the methodology, and the paper suggests several avenues for future research.
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Jangraw DC, Keren H, Sun H, Bedder RL, Rutledge RB, Pereira F, Thomas AG, Pine DS, Zheng C, Nielson DM, Stringaris A. A highly replicable decline in mood during rest and simple tasks. Nat Hum Behav 2023; 7:596-610. [PMID: 36849591 PMCID: PMC10192073 DOI: 10.1038/s41562-023-01519-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 01/04/2023] [Indexed: 03/01/2023]
Abstract
Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.
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Affiliation(s)
- David C Jangraw
- National Institute of Mental Health, Bethesda, MD, USA.
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, USA.
| | - Hanna Keren
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Haorui Sun
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, USA
| | - Rachel L Bedder
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Robb B Rutledge
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | | | - Adam G Thomas
- National Institute of Mental Health, Bethesda, MD, USA
| | - Daniel S Pine
- National Institute of Mental Health, Bethesda, MD, USA
| | - Charles Zheng
- National Institute of Mental Health, Bethesda, MD, USA
| | | | - Argyris Stringaris
- Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
- Faculty of Brain Sciences, Division of Psychiatry, University College London, London, UK
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3
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Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification. STAT METHOD APPL-GER 2022. [DOI: 10.1007/s10260-022-00658-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
AbstractWe consider predictions in longitudinal studies, and investigate the well known statistical mixed-effects model, piecewise linear mixed-effects model and six different popular machine learning approaches: decision trees, bagging, random forest, boosting, support-vector machine and neural network. In order to consider the correlated data in machine learning, the random effects is combined into the traditional tree methods and random forest. Our focus is the performance of statistical modelling and machine learning especially in the cases of the misspecification of the fixed effects and the random effects. Extensive simulation studies have been carried out to evaluate the performance using a number of criteria. Two real datasets from longitudinal studies are analysed to demonstrate our findings. The R code and dataset are freely available at https://github.com/shuwen92/MEML.
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Altinisik Y. Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models. J Appl Stat 2022; 50:408-433. [PMID: 36698542 PMCID: PMC9870003 DOI: 10.1080/02664763.2022.2096875] [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: 01/28/2023]
Abstract
Unobserved heterogeneity causing overdispersion and the excessive number of zeros take a prominent place in the methodological development on count modeling. An insight into the mechanisms that induce heterogeneity is required for better understanding of the phenomenon of overdispersion. When the heterogeneity is sourced by the stochastic component of the model, the use of a heterogenous Poisson distribution for this part encounters as an elegant solution. Hierarchical design of the study is also responsible for the heterogeneity as the unobservable effects at various levels also contribute to the overdispersion. Zero-inflation, heterogeneity and multilevel nature in the count data present special challenges in their own respect, however the presence of all in one study adds more challenges to the modeling strategies. This study therefore is designed to merge the attractive features of the separate strand of the solutions in order to face such a comprehensive challenge. This study differs from the previous attempts by the choice of two recently developed heterogeneous distributions, namely Poisson-Lindley (PL) and Poisson-Ailamujia (PA) for the truncated part. Using generalized linear mixed modeling settings, predictive performances of the multilevel PL and PA models and their hurdle counterparts were assessed within a comprehensive simulation study in terms of bias, precision and accuracy measures. Multilevel models were applied to two separate real world examples for the assessment of practical implications of the new models proposed in this study.
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Affiliation(s)
- Yasin Altinisik
- Department of Statistics, Faculty of Science and Literature, Sinop University, Sinop, Turkey,Yasin Altinisik
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Chen G, Lewis VA, Gottlieb DJ, O'Malley AJ. Using a mixed-effect model with a parameter-space of heterogenous dimension to evaluate whether accountable care organizations are associated with greater uniformity across constituent practices. Stat Med 2022; 41:4215-4226. [PMID: 35760495 DOI: 10.1002/sim.9506] [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: 03/25/2021] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 11/11/2022]
Abstract
Accountable care organization (ACO) legislation was designed to improve patient outcomes by inducing greater coordination of care and adoption of best practices. Therefore, it is of interest to assess whether greater uniformity occurs among practices comprising an ACO post ACO formation. We develop a mixed-effect model with a difference-in-difference design to evaluate the effect of a patient receiving care from an ACO on patient outcomes and adapt this model to examine whether an ACO is associated with increased uniformity across its constituent practices. The task is complicated by the organizations within an ACO forming an additional layer in the multilevel model, due to medical practices and hospitals that form an ACOs being nested within the ACO, making the number of levels of the model variable and the dimension of the parameter space time-varying. We develop the model and a procedure for testing the hypothesis that ACO formation was associated with increased uniformity among its constituent practices. We apply our procedure to a cohort of medicare beneficiaries followed over 2009-2014. Although there is extensive heterogeneity of becoming an ACOs across practices, we find that the formation of an ACO appears to be associated with greater uniformity of patient outcomes among its constituent practices.
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Affiliation(s)
- Guanqing Chen
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Valerie A Lewis
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Daniel J Gottlieb
- White River Junction VA Medical Center, White River Junction, Vermont, USA
| | - A James O'Malley
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.,The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA
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Abstract
Reproducibility in biomedical research, and more specifically in preclinical animal research, has been seriously questioned. Several cases of spectacular failures to replicate findings published in the primary scientific literature have led to a perceived reproducibility crisis. Diverse threats to reproducibility have been proposed, including lack of scientific rigour, low statistical power, publication bias, analytical flexibility and fraud. An important aspect that is generally overlooked is the lack of external validity caused by rigorous standardization of both the animals and the environment. Here, we argue that a reaction norm approach to phenotypic variation, acknowledging gene-by-environment interactions, can help us seeing reproducibility of animal experiments in a new light. We illustrate how dominating environmental effects can affect inference and effect size estimates of studies and how elimination of dominant factors through standardization affects the nature of the expected phenotype variation through the reaction norms of small effect. Finally, we discuss the consequences of reaction norms of small effect for statistical analysis, specifically for random effect latent variable models and the random lab model.
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Affiliation(s)
- Bernhard Voelkl
- Animal Welfare Division, University of Bern, Laenggassstrasse 120, 3012, Bern, Switzerland.
| | - Hanno Würbel
- Animal Welfare Division, University of Bern, Laenggassstrasse 120, 3012, Bern, Switzerland
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Schielzeth H, Dingemanse NJ, Nakagawa S, Westneat DF, Allegue H, Teplitsky C, Réale D, Dochtermann NA, Garamszegi LZ, Araya‐Ajoy YG. Robustness of linear mixed‐effects models to violations of distributional assumptions. Methods Ecol Evol 2020. [DOI: 10.1111/2041-210x.13434] [Citation(s) in RCA: 234] [Impact Index Per Article: 58.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Holger Schielzeth
- Institute of Ecology and Evolution Friedrich Schiller University Jena Germany
| | - Niels J. Dingemanse
- Behavioural Ecology Department of Biology Ludwig‐Maximilians University of Munich Planegg‐Martinsried Germany
| | - Shinichi Nakagawa
- Evolution & Ecology Research Centre and School of Biological, Earth and Environmental Sciences University of New South Wales Sydney NSW Australia
| | | | - Hassen Allegue
- Département des Sciences Biologiques Université du Québec à Montréal Montreal QC Canada
| | - Céline Teplitsky
- Centre d'Ecologie Fonctionnelle et Evolutive CNRS Montpellier France
| | - Denis Réale
- Département des Sciences Biologiques Université du Québec à Montréal Montreal QC Canada
| | - Ned A. Dochtermann
- Department of Biological Sciences North Dakota State University Fargo ND USA
| | - László Zsolt Garamszegi
- Centre for Ecological ResearchInstitute of Ecology and Botany Vácrátót Hungary
- MTA‐ELTE Theoretical Biology and Evolutionary Ecology Research Group Department of Plant Systematics, Ecology and Theoretical Biology Eötvös Loránd University Budapest Hungary
| | - Yimen G. Araya‐Ajoy
- Centre for Biodiversity Dynamics (CBD) Department of Biology Norwegian University of Science and Technology (NTNU) Trondheim Norway
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Hui FKC, Müller S, Welsh AH. Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models. Int Stat Rev 2020. [DOI: 10.1111/insr.12378] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Francis K. C. Hui
- Research School of Finance, Actuarial Studies & Statistics Australian National University Canberra Australia
| | - Samuel Müller
- School of Mathematics and Statistics The University of Sydney Sydney Australia
| | - Alan H. Welsh
- Research School of Finance, Actuarial Studies & Statistics Australian National University Canberra Australia
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McNeish D. Effect Partitioning in Cross-Sectionally Clustered Data Without Multilevel Models. MULTIVARIATE BEHAVIORAL RESEARCH 2019; 54:906-925. [PMID: 31021178 DOI: 10.1080/00273171.2019.1602504] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Effect partitioning is almost exclusively performed with multilevel models (MLMs) - so much so that some have considered the two to be synonymous. MLMs are able to provide estimates with desirable statistical properties when data come from a hierarchical structure; but the random effects included in MLMs are not always integral to the analysis. As a result, other methods with relaxed assumptions are viable options in many cases. Through empirical examples and simulations, we show how generalized estimating equations (GEEs) can be used to effectively partition effects without random effects. We show that more onerous steps of MLMs such as determining the number of random effects and the structure for their covariance can be bypassed with GEEs while still obtaining identical or near-identical results. Additionally, violations of distributional assumptions adversely affect estimates with MLMs but have no effect on GEEs because no such assumptions are made. This makes GEEs a flexible alternative to MLMs with minimal assumptions that may warrant consideration. Limitations of GEEs for partitioning effects are also discussed.
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11
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Yu S, Huang X. Random-intercept misspecification in generalized linear mixed models for binary responses. STAT METHOD APPL-GER 2017. [DOI: 10.1007/s10260-017-0376-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Grilli L, Pennoni F, Rampichini C, Romeo I. Exploiting TIMSS and PIRLS combined data: Multivariate multilevel modelling of student achievement. Ann Appl Stat 2016. [DOI: 10.1214/16-aoas988] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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13
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Cam E, Aubry LM, Authier M. The Conundrum of Heterogeneities in Life History Studies. Trends Ecol Evol 2016; 31:872-886. [DOI: 10.1016/j.tree.2016.08.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Revised: 08/17/2016] [Accepted: 08/18/2016] [Indexed: 12/21/2022]
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Helgeland J, Kristoffersen DT, Skyrud KD, Lindman AS. Variation between Hospitals with Regard to Diagnostic Practice, Coding Accuracy, and Case-Mix. A Retrospective Validation Study of Administrative Data versus Medical Records for Estimating 30-Day Mortality after Hip Fracture. PLoS One 2016; 11:e0156075. [PMID: 27203243 PMCID: PMC4874695 DOI: 10.1371/journal.pone.0156075] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 05/09/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The purpose of this study was to assess the validity of patient administrative data (PAS) for calculating 30-day mortality after hip fracture as a quality indicator, by a retrospective study of medical records. METHODS We used PAS data from all Norwegian hospitals (2005-2009), merged with vital status from the National Registry, to calculate 30-day case-mix adjusted mortality for each hospital (n = 51). We used stratified sampling to establish a representative sample of both hospitals and cases. The hospitals were stratified according to high, low and medium mortality of which 4, 3, and 5 hospitals were sampled, respectively. Within hospitals, cases were sampled stratified according to year of admission, age, length of stay, and vital 30-day status (alive/dead). The final study sample included 1043 cases from 11 hospitals. Clinical information was abstracted from the medical records. Diagnostic and clinical information from the medical records and PAS were used to define definite and probable hip fracture. We used logistic regression analysis in order to estimate systematic between-hospital variation in unmeasured confounding. Finally, to study the consequences of unmeasured confounding for identifying mortality outlier hospitals, a sensitivity analysis was performed. RESULTS The estimated overall positive predictive value was 95.9% for definite and 99.7% for definite or probable hip fracture, with no statistically significant differences between hospitals. The standard deviation of the additional, systematic hospital bias in mortality estimates was 0.044 on the logistic scale. The effect of unmeasured confounding on outlier detection was small to moderate, noticeable only for large hospital volumes. CONCLUSIONS This study showed that PAS data are adequate for identifying cases of hip fracture, and the effect of unmeasured case mix variation was small. In conclusion, PAS data are adequate for calculating 30-day mortality after hip-fracture as a quality indicator in Norway.
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Affiliation(s)
- Jon Helgeland
- Quality Measurement Unit, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Katrine Damgaard Skyrud
- Department of Registration, Institute of Population-Based Cancer Research, Cancer Registry of Norway, Oslo, Norway
| | - Anja Schou Lindman
- Quality Measurement Unit, Norwegian Institute of Public Health, Oslo, Norway
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