Autoregressive errors with a repeated measures design in clinical trials.
Repeated measures designs are common in clinical trials where recordings on a patient are made repeatedly over time. A basic assumption for the analysis of repeated measures designs is that a common correlation structure between observations for a given patient exists. When several observations are made for each patient this assumption may be tenuous at best. This article presents a pragmatic approach for combining a repeated measures design with a first-order autoregressive error component. A method of filtering the observed data to account for the autoregressive structure of the errors is considered. The effect on the analysis of variance results after extraction of the autoregressive component is seen by comparison of ANOVA summaries.
D000704:Analysis of Variance; D000889:Anti-Arrhythmia Agents / Q000008:administration & dosage; D002986:Clinical Trials as Topic / Q000379:methods*; D006801:Humans; D011897:Random Allocation
D000889:Anti-Arrhythmia Agents