Borrowing from supplemental sources to estimate causal effects from a primary data source.

ABSTRACT: The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regressio

SUBMITTER: Boatman JA 

PROVIDER: S-EPMC11667477 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature