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Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals

Author

Listed:
  • Issa J. Dahabreh
  • Sarah E. Robertson
  • Eric J. Tchetgen
  • Elizabeth A. Stuart
  • Miguel A. Hernán

Abstract

We consider methods for causal inference in randomized trials nested within cohorts of trial‐eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite‐sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial‐eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.

Suggested Citation

  • Issa J. Dahabreh & Sarah E. Robertson & Eric J. Tchetgen & Elizabeth A. Stuart & Miguel A. Hernán, 2019. "Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals," Biometrics, The International Biometric Society, vol. 75(2), pages 685-694, June.
    • Handle: RePEc:bla:biomet:v:75:y:2019:i:2:p:685-694
    DOI: 10.1111/biom.13009
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    Cited by:

    1. Naoki Egami & Erin Hartman, 2021. "Covariate selection for generalizing experimental results: Application to a large‐scale development program in Uganda," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1524-1548, October.
    2. Melody Y Huang & Harsh Parikh, 2024. "Towards Generalizing Inferences from Trials to Target Populations," Papers 2402.17042, arXiv.org, revised May 2024.
    3. Bing Li & Constantine Gatsonis & Issa J. Dahabreh & Jon A. Steingrimsson, 2023. "Estimating the area under the ROC curve when transporting a prediction model to a target population," Biometrics, The International Biometric Society, vol. 79(3), pages 2382-2393, September.
    4. Masahiro Kato & Masatoshi Uehara & Shota Yasui, 2020. "Off-Policy Evaluation and Learning for External Validity under a Covariate Shift," Papers 2002.11642, arXiv.org, revised Oct 2020.
    5. Colnet Bénédicte & Josse Julie & Varoquaux Gaël & Scornet Erwan, 2022. "Causal effect on a target population: A sensitivity analysis to handle missing covariates," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 372-414, January.
    6. Benjamin Lu & Eli Ben-Michael & Avi Feller & Luke Miratrix, 2023. "Is It Who You Are or Where You Are? Accounting for Compositional Differences in Cross-Site Treatment Effect Variation," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 420-453, August.
    7. Bo Zhang, 2023. "Efficient algorithms for building representative matched pairs with enhanced generalizability," Biometrics, The International Biometric Society, vol. 79(4), pages 3981-3997, December.

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