Generalizability of Subgroup Effects
| Autor: | Ian Schmid, Marissa J. Seamans, Benjamin Ackerman, Elizabeth A. Stuart, Hwanhee Hong |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
Sample average Epidemiology Average treatment effect Population Estimator Sample (statistics) Target population 01 natural sciences Causality Article 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Bias Econometrics Humans Computer Simulation Generalizability theory 030212 general & internal medicine 0101 mathematics Psychology education Monte Carlo Method |
| Zdroj: | Epidemiology |
| ISSN: | 1044-3983 |
| DOI: | 10.1097/ede.0000000000001329 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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