Generalizability of Subgroup Effects.
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.
Duke Scholars
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- Monte Carlo Method
- Humans
- Epidemiology
- Computer Simulation
- Bias
- 4905 Statistics
- 4206 Public health
- 4202 Epidemiology
- 1117 Public Health and Health Services
- 0104 Statistics
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
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