20: Integrate Meta-analysis into Specific Study for Estimating Conditional Average Treatment Effect

Music City Center 

Randomized controlled trials are the standard method for estimating causal effects, ensuring statistical power and confidence through adequate sample sizes. However, achieving sufficient sample sizes is often challenging. This study proposes a novel method to estimate the average treatment effect (ATE) in a target population by integrating and reconstructing information from previous trials with only summary statistics of outcomes and covariates via meta-analysis. The proposed approach combines meta-analysis, transfer learning, and weighted regression. Unlike existing methods, which estimate the ATE based on the distribution of source trials, our method directly estimates the ATE for the target population. The proposed method requires only the means and variances of outcomes and covariates from the source trials and is theoretically valid under the covariate shift assumption, regardless of the distribution of covariates in the source trials. Simulations and real-data analyses demonstrate that the proposed method yields a consistent estimator and achieves higher statistical power than the estimator derived solely from the target trial.

conditional average treatment effect

meta-analysis

transfer learning

weighted linear regression 

Section on Statistics in Epidemiology