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Erschienen in: European Journal of Epidemiology 8/2019

19.06.2019 | COMMENTARY

Extending inferences from a randomized trial to a target population

verfasst von: Issa J. Dahabreh, Miguel A. Hernán

Erschienen in: European Journal of Epidemiology | Ausgabe 8/2019

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Excerpt

In this issue, Weiss discusses “generalizing” inferences from randomized trials to other populations [1]. However, he does not explicitly define what “generalizing” means, assumes that “generalizing” the results of a randomized trial has a single goal, and reduces generalizability to a binary subjective judgment—findings are either generalizable or not generalizable. A growing literature (e.g.,  [113])  precisely defines the several meanings and goals of extending inferences from randomized trials to another population, and describes analyses whose findings go beyond simple binary judgements. Here, we provide a non-technical overview of this literature. First, we briefly review the main concepts, then we outline the available study designs and statistical approaches. …
Literatur
2.
Zurück zum Zitat Cole SR, Stuart EA. Generalizing evidence from randomized clinical trials to target populations: the ACTG 320 trial. Am J Epidemiol. 2010;172(1):107–15.CrossRefPubMedPubMedCentral Cole SR, Stuart EA. Generalizing evidence from randomized clinical trials to target populations: the ACTG 320 trial. Am J Epidemiol. 2010;172(1):107–15.CrossRefPubMedPubMedCentral
3.
Zurück zum Zitat Stuart EA, Cole SR, Bradshaw CP, Leaf PJ. The use of propensity scores to assess the generalizability of results from randomized trials. J R Stat Soc Ser A. 2011;174(2):369–86.CrossRef Stuart EA, Cole SR, Bradshaw CP, Leaf PJ. The use of propensity scores to assess the generalizability of results from randomized trials. J R Stat Soc Ser A. 2011;174(2):369–86.CrossRef
4.
Zurück zum Zitat Tipton E. Improving generalizations from experiments using propensity score subclassification: assumptions, properties, and contexts. J Educ Behav Stat. 2013;38(3):239–66.CrossRef Tipton E. Improving generalizations from experiments using propensity score subclassification: assumptions, properties, and contexts. J Educ Behav Stat. 2013;38(3):239–66.CrossRef
5.
Zurück zum Zitat O’Muircheartaigh C, Hedges LV. Generalizing from unrepresentative experiments: a stratified propensity score approach. J R Stat Soc Ser C. 2014;63(2):195–210.CrossRef O’Muircheartaigh C, Hedges LV. Generalizing from unrepresentative experiments: a stratified propensity score approach. J R Stat Soc Ser C. 2014;63(2):195–210.CrossRef
6.
Zurück zum Zitat Hartman E, Grieve R, Ramsahai R, Sekhon JS. From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects. J R Stat Soc Ser A. 2015;178(3):757–78.CrossRef Hartman E, Grieve R, Ramsahai R, Sekhon JS. From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects. J R Stat Soc Ser A. 2015;178(3):757–78.CrossRef
8.
Zurück zum Zitat Buchanan AL, Hudgens MG, Cole SR, et al. Generalizing evidence from randomized trials using inverse probability of sampling weights. J R Stat Soc Ser A Stat Soc. 2018;181(4):1193–209.CrossRefPubMed Buchanan AL, Hudgens MG, Cole SR, et al. Generalizing evidence from randomized trials using inverse probability of sampling weights. J R Stat Soc Ser A Stat Soc. 2018;181(4):1193–209.CrossRefPubMed
9.
Zurück zum Zitat Zhang Z, Nie L, Soon G, Hu Z. New methods for treatment effect calibration, with applications to non-inferiority trials. Biometrics. 2016;72(1):20–9.CrossRefPubMed Zhang Z, Nie L, Soon G, Hu Z. New methods for treatment effect calibration, with applications to non-inferiority trials. Biometrics. 2016;72(1):20–9.CrossRefPubMed
10.
Zurück zum Zitat Rudolph KE, van der Laan MJ. Robust estimation of encouragement design intervention effects transported across sites. J R Stat Soc Ser B. 2017;79(5):1509–25.CrossRef Rudolph KE, van der Laan MJ. Robust estimation of encouragement design intervention effects transported across sites. J R Stat Soc Ser B. 2017;79(5):1509–25.CrossRef
11.
Zurück zum Zitat Westreich D, Edwards JK, Lesko CR, Stuart E, Cole SR. Transportability of trial results using inverse odds of sampling weights. Am J Epidemiol. 2017;186(8):1010–4.CrossRefPubMedPubMedCentral Westreich D, Edwards JK, Lesko CR, Stuart E, Cole SR. Transportability of trial results using inverse odds of sampling weights. Am J Epidemiol. 2017;186(8):1010–4.CrossRefPubMedPubMedCentral
12.
Zurück zum Zitat Dahabreh IJ, Robertson SE, Stuart EA, Hernán MA. Transporting inferences from a randomized trial to a new target population. arXiv preprint arXiv:1805.00550. 2018. Dahabreh IJ, Robertson SE, Stuart EA, Hernán MA. Transporting inferences from a randomized trial to a new target population. arXiv preprint arXiv:​1805.​00550. 2018.
13.
Zurück zum Zitat Chan W. Partially identified treatment effects for generalizability. J. Res. Educ. Effect. 2017;10(3):646–69. Chan W. Partially identified treatment effects for generalizability. J. Res. Educ. Effect. 2017;10(3):646–69.
14.
Zurück zum Zitat Dahabreh IJ, Robins JM, Haneuse SJ, Hernán MA. Generalizing causal inferences from randomized trials: counterfactual and graphical identification. 2019 (forthcoming). Dahabreh IJ, Robins JM, Haneuse SJ, Hernán MA. Generalizing causal inferences from randomized trials: counterfactual and graphical identification. 2019 (forthcoming).
15.
Zurück zum Zitat Hernán MA. Discussion of “Perils and potentials of self-selected entry to epidemiological studies and surveys” by N Keiding and TA Louis. J R Stat Soc Ser A Stat Soc. 2016;179(2):346–7.CrossRef Hernán MA. Discussion of “Perils and potentials of self-selected entry to epidemiological studies and surveys” by N Keiding and TA Louis. J R Stat Soc Ser A Stat Soc. 2016;179(2):346–7.CrossRef
16.
Zurück zum Zitat Heckman JJ. Randomization and social policy evaluation. Cambridge: National Bureau of Economic Research; 1991.CrossRef Heckman JJ. Randomization and social policy evaluation. Cambridge: National Bureau of Economic Research; 1991.CrossRef
17.
Zurück zum Zitat Lesko CR, Buchanan AL, Westreich D, Edwards JK, Hudgens MG, Cole SR. Generalizing study results: a potential outcomes perspective. Epidemiol. 2017;28(4):553–61.CrossRefPubMedPubMedCentral Lesko CR, Buchanan AL, Westreich D, Edwards JK, Hudgens MG, Cole SR. Generalizing study results: a potential outcomes perspective. Epidemiol. 2017;28(4):553–61.CrossRefPubMedPubMedCentral
19.
Zurück zum Zitat Dahabreh IJ, Hernán MA, Robertson SE, Buchanan A, Steingrimsson JA. Generalizing trial findings in nested trial designs with sub-sampling of non-randomized individuals. arXiv preprint arXiv:1902.06080. 2019. Dahabreh IJ, Hernán MA, Robertson SE, Buchanan A, Steingrimsson JA. Generalizing trial findings in nested trial designs with sub-sampling of non-randomized individuals. arXiv preprint arXiv:​1902.​06080. 2019.
20.
Zurück zum Zitat Dahabreh IJ, Haneuse SJPA, Robins JM, Robertson SE, Buchanan AL, Stuart EA, et al. Study designs for extending causal inferences from a randomized trial to a target population. 2019. arXiv preprint arXiv:1905.07764. Dahabreh IJ, Haneuse SJPA, Robins JM, Robertson SE, Buchanan AL, Stuart EA, et al. Study designs for extending causal inferences from a randomized trial to a target population. 2019. arXiv preprint arXiv:​1905.​07764.
21.
Zurück zum Zitat Kern HL, Stuart EA, Hill J, Green DP. Assessing methods for generalizing experimental impact estimates to target populations. J Res Educ Effect. 2016;9(1):103–27. Kern HL, Stuart EA, Hill J, Green DP. Assessing methods for generalizing experimental impact estimates to target populations. J Res Educ Effect. 2016;9(1):103–27.
22.
Zurück zum Zitat Hernán MA, Robins JM. Causal inference. Boca Raton: Chapman & Hall/CRC; 2019, forthcoming. Hernán MA, Robins JM. Causal inference. Boca Raton: Chapman & Hall/CRC; 2019, forthcoming.
23.
Zurück zum Zitat Robins JM, Rotnitzky A, Scharfstein DO. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. Statistical models in epidemiology, the environment, and clinical trials: Springer; 2000. p. 1–94. Robins JM, Rotnitzky A, Scharfstein DO. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. Statistical models in epidemiology, the environment, and clinical trials: Springer; 2000. p. 1–94.
24.
Zurück zum Zitat Nguyen TQ, Ebnesajjad C, Cole SR, Stuart EA. Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects. Ann Appl Stat. 2017;11(1):225–47.CrossRef Nguyen TQ, Ebnesajjad C, Cole SR, Stuart EA. Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects. Ann Appl Stat. 2017;11(1):225–47.CrossRef
26.
Zurück zum Zitat Dahabreh IJ, Robins JM, Haneuse SJ, et al. Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population. arXiv preprint arXiv:1905.10684. 2019. Dahabreh IJ, Robins JM, Haneuse SJ, et al. Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population. arXiv preprint arXiv:​1905.​10684. 2019.
Metadaten
Titel
Extending inferences from a randomized trial to a target population
verfasst von
Issa J. Dahabreh
Miguel A. Hernán
Publikationsdatum
19.06.2019
Verlag
Springer Netherlands
Erschienen in
European Journal of Epidemiology / Ausgabe 8/2019
Print ISSN: 0393-2990
Elektronische ISSN: 1573-7284
DOI
https://doi.org/10.1007/s10654-019-00533-2

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