A Review of Generalizability and Transportability
By Irina Degtiar, Sherri Rose. Annual Review of Statistics and Its Application, 2021
343 citations recorded by the source corpus.
When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This article presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, and the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 10 is March 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/314a40478980c610e0992cfb843cd4578daa4ac0
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Citations in the corpus
- The next generation of evidence-based medicine (2023, positive): “Evidence generation using transportability analysis is gaining traction and refers to the ability to generalize inferences from a study sample in one country to a target population in another country where the study was not conducted 47,48 .”