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

TL;DR

This paper develops sensitivity analysis methods for generalizing treatment effects from randomized trials to target populations when some effect modifiers are unobserved in the target group, enhancing the reliability of such generalizations.

Contribution

It introduces new sensitivity analysis techniques that handle unobserved effect modifiers in the target population, using outcome models and weighting adjustments.

Findings

Methods accommodate linear and multiplicative outcome models

Illustration with HIV treatment trial demonstrates practical application

Provides detailed implementation guidance

Abstract

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.