A SIMULATION STUDY OF ESTIMATORS FOR RATES OF CHANGE IN LONGITUDINAL-STUDIES WITH ATTRITION

Many longitudinal studies and clinical trials are designed to compare rates of change over time in one or more outcome variables in several groups. Most such studies have incomplete data because some patients d rop out before completing the study. The missing data may induce bias and inefficiency in naive estimates of important parameters. This pape r uses Monte Carlo methods to compare the bias and efficiency of sever al two-stage estimators of the effect of treatment on the mean rate of change when the missing data arise from one of four processes. We als o study the validity of confidence intervals and the power of hypothes is tests based on these estimates and their standard errors, In genera l, the weighted feast squares estimator does relatively well, as does an analysis of covariance type estimator proposed by Wu et al. The bes t estimates of variance components are based on complete cases or maxi mum likelihood.