title: Estimating the effect of multiple imputation on incomplete longitudinal data with application to a randomized clinical study.
abstract: For analyzing incomplete longitudinal data, there has been recent interest in comparing estimates with and without the use of multiple imputation along with mixed effects model and generalized estimating equations. Empirically, the additional use of multiple imputation generally led to overestimated variances and may yield more heavily biased estimates than the use of last observation carried forward. Under ignorable or nonignorable missing values, a mixed effects model or generalized estimating equations alone yielded more unbiased estimates. The different methods were also assessed in a randomized controlled clinical trial.
authors: School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, PR China. dytfong@hku.hk
authors: Daniel Y T
authors: DY
authors: Fong
authors: Shesh N
authors: SN
authors: Rai
authors: Karen S L
authors: KS
authors: Lam
mesh_terms: D015982:Bias; D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010349:Patient Compliance; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D016896:Treatment Outcome