{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["56(5)"],"submitter":["Zeldow B"],"pubmed_abstract":["<h4>Objective</h4>To define confounding bias in difference-in-difference studies and compare regression- and matching-based estimators designed to correct bias due to observed confounders.<h4>Data sources</h4>We simulated data from linear models that incorporated different confounding relationships: time-invariant covariates with a time-varying effect on the outcome, time-varying covariates with a constant effect on the outcome, and time-varying covariates with a time-varying effect on the outcome. We considered a simple setting that is common in the applied literature: treatment is introduced at a single time point and there is no unobserved treatment effect heterogeneity.<h4>Study design</h4>We compared the bias and root mean squared error of treatment effect estimates from six model spe"],"journal":["Health services research"],"pagination":["932-941"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8522571"],"repository":["biostudies-literature"],"pubmed_title":["Confounding and regression adjustment in difference-in-differences studies."],"pmcid":["PMC8522571"],"pubmed_authors":["Hatfield LA","Zeldow B"],"additional_accession":[]},"is_claimable":false,"name":"Confounding and regression adjustment in difference-in-differences studies.","description":"<h4>Objective</h4>To define confounding bias in difference-in-difference studies and compare regression- and matching-based estimators designed to correct bias due to observed confounders.<h4>Data sources</h4>We simulated data from linear models that incorporated different confounding relationships: time-invariant covariates with a time-varying effect on the outcome, time-varying covariates with a constant effect on the outcome, and time-varying covariates with a time-varying effect on the outcome. We considered a simple setting that is common in the applied literature: treatment is introduced at a single time point and there is no unobserved treatment effect heterogeneity.<h4>Study design</h4>We compared the bias and root mean squared error of treatment effect estimates from six model spe","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-04T10:21:26.129Z","creation":"2025-04-04T10:21:26.129Z"},"accession":"S-EPMC8522571","cross_references":{"pubmed":["33978956"],"doi":["10.1111/1475-6773.13666"]}}