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Estimating transformations for repeated measures modeling of continuous bounded outcome data
Author(s) -
Hutmacher Matthew M.,
French Jonathan L.,
Krishnaswami Sriram,
Me Sujatha
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4155
Subject(s) - outcome (game theory) , censoring (clinical trials) , random effects model , residual , bounded function , econometrics , computer science , covariate , statistics , mathematics , medicine , algorithm , mathematical economics , mathematical analysis , meta analysis
Continuous bounded outcome data are unlikely to meet the usual assumptions for mixed‐effects models of normally distributed and independent subject‐specific and residual random effects. Additionally, overly complicated model structures might be necessary to account adequately for non‐drug (time‐dependent) and drug treatment effects. A transformation strategy with a likelihood component for censoring is developed to promote the simplicity of model structures and to improve the plausibility of assumptions on the random effects. The approach is motivated by Health Assessment Questionnaire Disability Index (HAQ‐DI) data from a study in subjects with rheumatoid arthritis and is evaluated using a simulation study. Copyright © 2011 John Wiley & Sons, Ltd.