Weighted Random Regression Models and Dropouts

Abstract

In studies with repeated measurements, one of the popular primary interests is the comparison of the rates of change in a response variable between groups. The random regression model (RRM) has been offered as a potential solution to statistical problems posed by dropouts in clinical trials. However, the power of RRM tests for differences in rates of change can be seriously reduced due to dropouts. We examine the effect of dropouts on the power of RRM tests for testing differences in the rates of change between two groups through simulation. We examine the performance of weighted random regression models, which assign equal weights to subjects, equal weights to measurements, and optimal weights that minimize the variance of the regression coefficient. We perform the simulation study to evaluate the performance of the above three weighting schemes using type I errors and the power in repeated measurements data as affected by different dropout mechanisms such as random dropouts and treatment-dependent dropouts.