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Generalizing Randomized Trial Findings to a Target Population using Complex Survey Population Data (307870)

Keywords: generalizability, causal inference, complex survey data, randomized trial,

Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform health policy, yet their results may not generalize well to a policy-relevant target population due to potential differences in effect moderators between the trial and population. Statistical methods have been developed to improve generalizability by combining trials and population data, and weighting the trial to resemble the population on baseline covariates. Large health surveys with complex survey designs are a logical source for population data; however, there is currently no best practice for incorporating survey weights when generalizing trial findings to a complex survey. We propose and investigate ways to incorporate survey weights in this context. We examine the performance of these methods (and of ignoring the complex survey design) in simulations and apply the methods to generalize findings from PREMIER, a lifestyle intervention trial, to a target population from NHANES. The work highlights the importance in properly accounting for the complex survey design when generalizing trial findings to a population represented by a complex survey sample.