TY - JOUR
T1 - Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial
T2 - Assumptions, models, effect scales, data scenarios, and implementation details
AU - Nguyen, Trang Quynh
AU - Ackerman, Benjamin
AU - Schmid, Ian
AU - Cole, Stephen R.
AU - Stuart, Elizabeth A.
N1 - Publisher Copyright:
© 2018 Nguyen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018/12
Y1 - 2018/12
N2 - Background Randomized controlled trials are often used to inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, may be different from the ATE observed in a trial if there are effect modifiers whose distribution in the target population is different that from that in the trial. Methods exist to use trial data to estimate the target population ATE, provided the distributions of treatment effect modifiers are observed in both the trial and target population—an assumption that may not hold in practice. Methods The proposed sensitivity analyses address the situation where a treatment effect modifier is observed in the trial but not the target population. These methods are based on an outcome model or the combination of such a model and weighting adjustment for observed differences between the trial sample and target population. They accommodate several types of outcome models: linear models (including single time outcome and pre- and post-treatment outcomes) for additive effects, and models with log or logit link for multiplicative effects. We clarify the methods’ assumptions and provide detailed implementation instructions. Illustration We illustrate the methods using an example generalizing the effects of an HIV treatment regimen from a randomized trial to a relevant target population. Conclusion These methods allow researchers and decision-makers to have more appropriate confidence when drawing conclusions about target population effects.
AB - Background Randomized controlled trials are often used to inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, may be different from the ATE observed in a trial if there are effect modifiers whose distribution in the target population is different that from that in the trial. Methods exist to use trial data to estimate the target population ATE, provided the distributions of treatment effect modifiers are observed in both the trial and target population—an assumption that may not hold in practice. Methods The proposed sensitivity analyses address the situation where a treatment effect modifier is observed in the trial but not the target population. These methods are based on an outcome model or the combination of such a model and weighting adjustment for observed differences between the trial sample and target population. They accommodate several types of outcome models: linear models (including single time outcome and pre- and post-treatment outcomes) for additive effects, and models with log or logit link for multiplicative effects. We clarify the methods’ assumptions and provide detailed implementation instructions. Illustration We illustrate the methods using an example generalizing the effects of an HIV treatment regimen from a randomized trial to a relevant target population. Conclusion These methods allow researchers and decision-makers to have more appropriate confidence when drawing conclusions about target population effects.
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U2 - 10.1371/journal.pone.0208795
DO - 10.1371/journal.pone.0208795
M3 - Article
C2 - 30533053
AN - SCOPUS:85058411511
SN - 1932-6203
VL - 13
JO - PloS one
JF - PloS one
IS - 12
M1 - e0208795
ER -