Analyzing Pre-Post Randomized Studies with One Post-Randomization Score Using Repeated Measures and ANCOVA Models

• Activity Number: 296 - SPEED: Biometrics - Methods and Application, Part 1
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Fei Wan*
• Companies: University of Arkansas for Medical Sciences
• Keywords: ANCOVA; Repeated Measures; Heteroskedasticity; Marginal treatment effect; Conditional treatment effect; Interaction

Abstract

The analysis of covariance (ANCOVA) or repeated measures (RM) models are often used to analyze pre-post randomized studies. We demonstrate the asymptotic equivalence between the ANCOVA and constrained RM estimators for the marginal treatment effect, and discuss the conditions under which ANCOVA needs to include a baseline score by treatment interaction term. In particular, an ANCOVA interaction model with a mean centered baseline score can assess both the marginal treatment effect and the heterogeneity in the conditional treatment effect. However, the ordinary least squares (OLS) based inference is not valid for unconditional inference because this interaction model typically has heteroskedastic errors and OLS treats the sample mean of the baseline score as a known parameter. We propose a heteroskedasticity consistent variance estimator for heteroskedastic ANCOVA. Our simulation studies demonstrate that the proposed method provide valid inferences for testing both the marginal treatment effect and the heterogeneity of treatment effect using an ANCOVA interaction model. We used an acupuncture headache trial to elucidate the proposed approaches.