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Generalizability of Subgroup Effects.

Publication ,  Journal Article
Seamans, MJ; Hong, H; Ackerman, B; Schmid, I; Stuart, EA
Published in: Epidemiology
May 1, 2021

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.

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Published In

Epidemiology

DOI

EISSN

1531-5487

Publication Date

May 1, 2021

Volume

32

Issue

3

Start / End Page

389 / 392

Location

United States

Related Subject Headings

  • Monte Carlo Method
  • Humans
  • Epidemiology
  • Computer Simulation
  • Bias
  • 4905 Statistics
  • 4206 Public health
  • 4202 Epidemiology
  • 1117 Public Health and Health Services
  • 0104 Statistics
 

Citation

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Seamans, M. J., Hong, H., Ackerman, B., Schmid, I., & Stuart, E. A. (2021). Generalizability of Subgroup Effects. Epidemiology, 32(3), 389–392. https://doi.org/10.1097/EDE.0000000000001329
Seamans, Marissa J., Hwanhee Hong, Benjamin Ackerman, Ian Schmid, and Elizabeth A. Stuart. “Generalizability of Subgroup Effects.Epidemiology 32, no. 3 (May 1, 2021): 389–92. https://doi.org/10.1097/EDE.0000000000001329.
Seamans MJ, Hong H, Ackerman B, Schmid I, Stuart EA. Generalizability of Subgroup Effects. Epidemiology. 2021 May 1;32(3):389–92.
Seamans, Marissa J., et al. “Generalizability of Subgroup Effects.Epidemiology, vol. 32, no. 3, May 2021, pp. 389–92. Pubmed, doi:10.1097/EDE.0000000000001329.
Seamans MJ, Hong H, Ackerman B, Schmid I, Stuart EA. Generalizability of Subgroup Effects. Epidemiology. 2021 May 1;32(3):389–392.

Published In

Epidemiology

DOI

EISSN

1531-5487

Publication Date

May 1, 2021

Volume

32

Issue

3

Start / End Page

389 / 392

Location

United States

Related Subject Headings

  • Monte Carlo Method
  • Humans
  • Epidemiology
  • Computer Simulation
  • Bias
  • 4905 Statistics
  • 4206 Public health
  • 4202 Epidemiology
  • 1117 Public Health and Health Services
  • 0104 Statistics