New methods for generalizability and transportability: the new norm

  • Sunni L. MumfordEmail author
  • Enrique F. Schisterman

Generalizability is an ever-present concern in epidemiology. Though randomized trials are often considered the gold standard, as Weiss describes there are often scenarios where results from randomized trials are not more broadly applicable and observational studies are needed to inform associations in populations not represented in randomized trials [1]. We recently completed a randomized trial that highlights the importance of generalizability and of considering the target population from the outset. The Effects of Aspirin in Gestation and Reproduction (EAGeR) trial was designed to evaluate the effect of low dose aspirin on pregnancy loss [2]. It is hypothesized that aspirin may improve blood flow in the uterus, thus facilitating implantation, and that this may be particularly beneficial for women who have recently had a pregnancy loss. Aspirin is well known for its anti-inflammatory properties, and as it is a low cost and widely available intervention, if it could be broadly applied...



This work was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health.


  1. 1.
    Weiss NS. Generalizing from the results of randomized studies of treatment: can non-randomized studies be of help? Eur J Epidemiol. 2019. Scholar
  2. 2.
    Schisterman EF, Silver RM, Perkins NJ, Mumford SL, Whitcomb BW, Stanford JB, Lesher LL, Faraggi D, Wactawski-Wende J, Browne RW, Townsend JM, White M, Lynch AM, Galai N. A randomised trial to evaluate the effects of low-dose aspirin in gestation and reproduction: design and baseline characteristics. Paediatr Perinat Epidemiol. 2013;27:598–609.CrossRefGoogle Scholar
  3. 3.
    Schisterman EF, Silver RM, Lesher LL, Faraggi D, Wactawski-Wende J, Townsend JM, Lynch AM, Perkins NJ, Mumford SL, Galai N. Preconception low-dose aspirin and pregnancy outcomes: results from the EAGeR randomised trial. Lancet. 2014;384:29–36.CrossRefPubMedCentralGoogle Scholar
  4. 4.
    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.CrossRefPubMedCentralGoogle Scholar
  5. 5.
    Stuart EA, Bradshaw CP, Leaf PJ. Assessing the generalizability of randomized trial results to target populations. Prev Sci. 2015;16:475–85.CrossRefPubMedCentralGoogle Scholar
  6. 6.
    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.CrossRefPubMedCentralGoogle Scholar
  7. 7.
    Lesko CR, Buchanan AL, Westreich D, Edwards JK, Hudgens MG, Cole SR. Generalizing study results: a potential outcomes perspective. Epidemiology. 2017;28:553–61.CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Buchanan AL, Hudgens MG, Cole SR, Mollan KR, Sax PE, Daar ES, Adimora AA, Eron JJ, Mugavero MJ. Generalizing evidence from randomized trials using inverse probability of sampling weights. J R Stat Soc Ser A Stat Soc. 2018;181(4):1193–209.CrossRefGoogle Scholar
  9. 9.
    Bareinboim E, Pearl J. A general algorithm for deciding transportability of experimental results. J Causal Inference. 2013;1(1):107–34.CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.Epidemiology Branch, Division of Intramural Population Health ResearchEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of HealthBethesdaUSA

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