Random regression with imputed values for dropouts

The random regression model (RRM) has been advocated as a potential solution to problems of statistical analysis posed by dropouts in clinical trials. However, the power of the RRM tests for differences in rates of change can be seriously attenuated by presence of dropouts. The use of imputed scores and other modifications are examined in an attempt to render a simple growth-curve form of the RRM analysis more robust against dropouts. Methods that extrapolate from an individual's own performance were found effective, although inclusion of time-in-treatment as a covariate was documented to be important under identifiable conditions. Of the methods evaluated, those that used group data to impute missing values for dropouts produced nonconservative bias. The results suggest the importance of careful evaluation of potential bias when integrating any group-based imputation procedure into the RRM analyses.