Date of Award


Degree Type


Degree Name

Master of Arts in Psychology


Behavioral Sciences



First Advisor

Manshu Yang


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.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.