Randomized controlled trials (RCTs) are considered the gold standard for estimating the causal effect of a drug or intervention in a study sample. However, while RCTs have strong internal validity, they often have weaker external validity, making it difficult to generalize trial results from a “non-representative” study sample to a broader population. This makes it challenging for policymakers to accurately draw population-level conclusions from trial evidence.
Given increasing concern about potential lack of generalizability of RCT findings, statistical methods have recently been proposed to estimate population average treatment effects by supplementing trial data with target population-level data. For my dissertation, I am conducting research on how to better assess and improve upon the external validity of randomized trials using these post-hoc quantitative methods. My work thus far has focused on synthesizing existing literature for non-statistician audiences, creating an R package for easy implementation of the methods, and deriving sensitivity analyses for unobserved effect modification. I am also developing methods to generalize RCT findings to a target population where the population-level data come from a complex survey, and therefore have survey weights relating the survey sample to the target population of interest.