Now you see it, now you don't: a comparison of traditional versus random-effects regression models in the analysis of longitudinal follow-up data from a clinical trial.
To illustrate the limitations of commonly used methods of handling missing data when using traditional analysis of variance (ANOVA) models and highlight the relative advantages of random-effects regression models, multiple analytic strategies were applied to follow-up data from a clinical trial. Traditional ANOVA and random-effects models produced similar results when underlying assumptions were met and data were complete. However, analyses based on subsamples, to which investigators would have been limited with traditional models, would have led to different conclusions about treatment effects over time than analyses based on intention-to-treat samples using random-effects regression models. These findings underscore the advantages of models that use all data collected and the importance of complete data collection to minimize sample bias.
Nich
C
C
Department of Psychiatry, School of Medicine, Yale University, New Haven, Connecticut 06519, USA.
Carroll
K
K
D000328:Adult; D000704:Analysis of Variance; D016009:Chi-Square Distribution; D002986:Clinical Trials as Topic / Q000592:standards / Q000706:statistics & numerical data*; D003042:Cocaine; D003430:Cross-Sectional Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D012044:Regression Analysis; D012494:Sampling Studies; D015983:Selection Bias; D019966:Substance-Related Disorders / Q000628:therapy; D013997:Time Factors; D016896:Treatment Outcome
D003042:Cocaine