Generalizability of subgroup effects

Marissa J. Seamans, Hwanhee Hong, Benjamin Ackerman, Ian Schmid, Elizabeth A. Stuart

Research output: Contribution to journalArticlepeer-review

Abstract

Generalizability methods are increasingly used to make inferences about the effect of interventions in target populations using a study sample. Most existing methods to generalize effects from sample to population rely on the assumption that subgroup-specific effects generalize directly. However, researchers may be concerned that in fact subgroup-specific effects differ between sample and population. In this brief report, we explore the generalizability of subgroup effects. First, we derive the bias in the sample average treatment effect estimator as an estimate of the population average treatment effect when subgroup effects in the sample do not directly generalize. Next, we present a Monte Carlo simulation to explore bias due to unmeasured heterogeneity of subgroup effects across sample and population. Finally, we examine the potential for bias in an illustrative data example. Understanding the generalizability of subgroup effects may lead to increased use of these methods for making externally valid inferences of treatment effects using a study sample.

Original languageEnglish (US)
Pages (from-to)389-392
Number of pages4
JournalEpidemiology
DOIs
StateAccepted/In press - 2021

Keywords

  • Bias
  • Causality
  • Effect modifier
  • Generalizability
  • Subgroup
  • Treatment effect heterogeneity

ASJC Scopus subject areas

  • Epidemiology

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