Generalizing randomized trial findings to a target population using complex survey population data.

Pubmed ID: 33241607

Pubmed Central ID: PMC8034867

Journal: Statistics in medicine

Publication Date: Feb. 28, 2021

Affiliation: Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

MeSH Terms: Humans, Substance-Related Disorders, Causality, Surveys and Questionnaires, Health Services Needs and Demand

Grants: R01 HL127491, K01 AA028193, P50 MH115842, R01 HL131606

Authors: Stuart EA, Siddique J, Lesko CR, Ackerman B, Susukida R

Cite As: Ackerman B, Lesko CR, Siddique J, Susukida R, Stuart EA. Generalizing randomized trial findings to a target population using complex survey population data. Stat Med 2021 Feb 28;40(5):1101-1120. Epub 2020 Nov 26.



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 through simulations in comparison 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.