  Randomized trials are considered the gold standard for estimating causal
effects. Trial findings are often used to inform policy and programming
efforts, yet their results may not generalize well to a 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-scale surveys in
fields such as health and education 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 our proposed estimator in simulations by
comparing its performance to estimators that ignore the complex survey
design.We then apply the methods to generalize findings from two trials - a
lifestyle intervention for blood pressure reduction and a web-based
intervention to treat substance use disorders - to their respective target
populations using population data from complex surveys. 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.
