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Generalizing evidence from randomized trials using inverse probability of sampling weights

Author

Listed:
  • Ashley L. Buchanan
  • Michael G. Hudgens
  • Stephen R. Cole
  • Katie R. Mollan
  • Paul E. Sax
  • Eric S. Daar
  • Adaora A. Adimora
  • Joseph J. Eron
  • Michael J. Mugavero

Abstract

Results obtained in randomized trials may not easily generalize to target populations. Whereas in randomized trials the treatment assignment mechanism is known, the sampling mechanism by which individuals are selected to participate in the trial is typically not known and assuming random sampling from the target population is often dubious. We consider an inverse probability of sampling weighted (IPSW) estimator for generalizing trial results to a target population. The IPSW estimator is shown to be consistent and asymptotically normal. A consistent sandwich‐type variance estimator is derived and simulation results are presented comparing the IPSW estimator with a previously proposed stratified estimator. The methods are then utilized to generalize results from two randomized trials of human immunodeficiency virus treatment to all people living with the disease in the USA.

Suggested Citation

  • Ashley L. Buchanan & Michael G. Hudgens & Stephen R. Cole & Katie R. Mollan & Paul E. Sax & Eric S. Daar & Adaora A. Adimora & Joseph J. Eron & Michael J. Mugavero, 2018. "Generalizing evidence from randomized trials using inverse probability of sampling weights," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1193-1209, October.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:4:p:1193-1209
    DOI: 10.1111/rssa.12357
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    References listed on IDEAS

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    Cited by:

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    2. Melody Y Huang & Harsh Parikh, 2024. "Towards Generalizing Inferences from Trials to Target Populations," Papers 2402.17042, arXiv.org, revised May 2024.
    3. Rui Chen & Guanhua Chen & Menggang Yu, 2023. "Entropy balancing for causal generalization with target sample summary information," Biometrics, The International Biometric Society, vol. 79(4), pages 3179-3190, December.
    4. Fan Li & Ashley L. Buchanan & Stephen R. Cole, 2022. "Generalizing trial evidence to target populations in non‐nested designs: Applications to AIDS clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 669-697, June.
    5. Frederico Finan & Demian Pouzo, 2021. "Reinforcing RCTs with Multiple Priors while Learning about External Validity," Papers 2112.09170, arXiv.org, revised Sep 2024.
    6. 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.
    7. Xinyu Li & Wang Miao & Fang Lu & Xiao‐Hua Zhou, 2023. "Improving efficiency of inference in clinical trials with external control data," Biometrics, The International Biometric Society, vol. 79(1), pages 394-403, March.
    8. Dasom Lee & Shu Yang & Lin Dong & Xiaofei Wang & Donglin Zeng & Jianwen Cai, 2023. "Improving trial generalizability using observational studies," Biometrics, The International Biometric Society, vol. 79(2), pages 1213-1225, June.

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