37: Improving Treatment Effect Precision in Randomized Controlled Trials by Leveraging Auxiliary Data

Music City Center 

Randomized controlled trials (RCTs) are the gold standard for evaluating treatment efficacy but often suffer from small sample sizes, leading to imprecise treatment effect estimates. Recent methods improve precision by incorporating large, observational auxiliary datasets with non-randomized but similar units. By training predictive models on these data, researchers can adjust for covariates and reduce variance without compromising randomization. While prior studies applied this approach to education experiments using same-source auxiliary data, we extend it to a medical RCT with external data. We analyzed the CHOICES (CTN-0055) RCT, which included 51 participants and assessed extended-release naltrexone (XR-NTX) for individuals with HIV and substance use disorders. Using the National Health and Nutrition Examination Survey (NHANES), we develop an auxiliary model predicting recent alcohol use. We will compare methods that integrate experimental and auxiliary data against standard estimators of XR-NTX's effect on alcohol use. We expect that incorporating auxiliary data will improve the precision of treatment effect estimates beyond what is achievable with standard RCT-based method.

Randomized Controlled Trials (RCTs)

Causal Inference

Treatment Effect Estimation

Observational Data Integration

Covariate Adjustment

Variance Reduction 

Biopharmaceutical Section