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

Benjamin Ackerman, Catherine R. Lesko, Juned Siddique, Ryoko Susukida, Elizabeth A. Stuart

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1101-1120
Number of pages20
JournalStatistics in Medicine
Volume40
Issue number5
DOIs
StatePublished - Feb 28 2021

Keywords

  • causal inference
  • complex survey data
  • generalizability
  • propensity scores
  • transportability

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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