Testing the Significance of Difference in Average Rates of Change in Controlled Longitudinal Studies With High Dropout Rates

Abstract

This article concerns methodology for testing the significance of differences in mean rates of change in controlled repeated measurements designs with limited sample sizes, autoregressive error structures, nonlinear patterns of underlying true mean change, dropout rates exceeding 50%, plus other missing data. Each of these is problematic for ordinary repeated measures analysis of variance, and a complex generalized linear mixed model formulation popularly advocated for the ability to deal with autoregressive error structures and missing data is shown to perform poorly in such circumstances. Monte Carlo simulation methods confirm that simple two-stage analyses of dropout-weighted linear slope coefficients provide conservative Type 1 error protection, although adequate power requires the presence of large treatment effects in studies with the limited sample sizes and high proportions of missing data. No other analysis has been documented to provide both conservative Type 1 error protection and competitive power under similarly taxing conditions.