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Estimating transformations for repeated measures modeling of continuous bounded outcome data.
MedLine Citation:
PMID:  21240881     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Matthew M Hutmacher; Jonathan L French; Sriram Krishnaswami; Sujatha Menon
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-1-13
Journal Detail:
Title:  Statistics in medicine     Volume:  -     ISSN:  1097-0258     ISO Abbreviation:  -     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2011-1-17     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Ann Arbor Pharmacometrics Group (A2PG), 110 E Miller, Garden Suite, Ann Arbor, MI 48104, U.S.A.
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