Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details

Public Deposited
Analytics

Downloadable Content

Download PDF
Request Version for Screen Reader
Creator
  • Edwards, J.K.
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Cole, S.R.
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Moore, R.D.
    • Other Affiliation: Johns Hopkins University
  • Mathews, W.C.
    • Other Affiliation: University of California San Diego
  • Kitahata, M.
    • Other Affiliation: University of Washington
  • Eron, J.J.
    • Affiliation: School of Medicine
Abstract
  • Background 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.
Date of publication
DOI
Identifier
Resource type
  • Article
Rights statement
  • In Copyright
Journal title
  • PLoS ONE
Journal volume
  • 13
Journal issue
  • 12
Page start
  • 1
Page end
  • 17
Language
  • English
ISSN
  • 1932-6203

Relations

Parents:

This work has no parents.

In Collection:

Items

Thumbnail Title Date Uploaded Visibility Actions
file details journal.pone.0208795.pdf 2020-08-07 Public Download