Date of Award
Master of Arts in Psychology
Randomized Control Trials (RCTs) are widely used in behavioral and health-related studies to evaluate the effectiveness of intervention strategies; however, missing data in RCTs are almost inevitable. In many RCT studies, the key focus is to examine the average treatment effect (ATE) within an entire population. Heterogenous treatment effects, often reflected in moderation effects of baseline personal attributes, do not typically get included in analyses. To handle missing data in RCTs, multiple imputation (MI) or inverse probability weighting (IPW) could be used. MI, although often preferred over IPW, may lead to biased ATE results when the probability of missingness depends on a moderator and the moderation effect is omitted from the imputation process. In contrast, IPW may produce imprecise results when the sample size is small. This study aims to evaluate the performance of MI via joint modeling (MI-JM), MI via chained equations (MI-CE), and IPW in estimating the ATE in RCTs with missing data and omitted moderation effects. A Monte Carlo simulation study is conducted to compare methods under various scenarios. Findings suggest that the use of MI-CE would be recommended across all study conditions with the presence of incomplete outcomes but fully observed covariates. IPW could be utilized with relatively large sample sizes and relatively a small number of covariates. Listwise deletion and MI-JM are not recommended for use in RCTs with missing data and omitted moderation effects.
Pauley, Elizabeth, "AN EVALUATION OF METHODS FOR HANDLING MISSING DATA IN RANDOMIZED CONTROLLED TRIALS WITH OMITTED MODERATION EFFECTS" (2023). Open Access Master's Theses. Paper 2404.