Drop-outs and a random regression model

Abstract

The implications of drop-outs for power of random regression model (RRM) tests of significance for differences in the rate of change produced by two treatments in a randomized parallel-groups design were investigated by Monte Carlo simulation methods. The two-stage RRM fitted a least squares linear regression equation to all of the available data for each individual, and then ANOVA or ANCOVA tests of significance were applied to the resulting slope coefficients. The tests of significance were adequately protected against type I error, but power was seriously eroded by the presence of drop-outs. Simple endpoint analyses with baseline and time-in-treatment covaried proved more robust against the power degradations.

Keywords:

• Random regression
• Endpoint analysis
• Drop-outs
• Power
• Type I error
• Bias
• Analysis of covariance
• Analysis of variance