A MULTIPLE IMPUTATION PROCEDURE FOR RECORD LINKAGE AND CAUSAL INFERENCE TO ESTIMATE THE EFFECTS OF HOME-DELIVERED MEALS.

ABSTRACT: Causal analysis of observational studies requires data that comprise of a set of covariates, a treatment assignment indicator, and the observed outcomes. However, data confidentiality restrictions or the nature of data collection may distribute these variables across two or more datasets. In the absence of unique identifiers to link records across files, probabilistic record linkage algorithms can be leveraged to merge the datasets. Current applications of record linkage are concerned with estimation of associations between variables that are exclusive to one file and not causal relationships. We propose a Bayesian framework for record linkage and causal inference where one file comprises all the covariate and observed outcome information, and the second file consists of a list of all indi

SUBMITTER: Shan M 

PROVIDER: S-EPMC9222523 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature