<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>56(5)</volume><submitter>Zeldow B</submitter><pubmed_abstract>&lt;h4>Objective&lt;/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.&lt;h4>Data sources&lt;/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.&lt;h4>Study design&lt;/h4>We compared the bias and root mean squared error of treatment effect estimates from six model spe</pubmed_abstract><journal>Health services research</journal><pagination>932-941</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8522571</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Confounding and regression adjustment in difference-in-differences studies.</pubmed_title><pmcid>PMC8522571</pmcid><pubmed_authors>Hatfield LA</pubmed_authors><pubmed_authors>Zeldow B</pubmed_authors></additional><is_claimable>false</is_claimable><name>Confounding and regression adjustment in difference-in-differences studies.</name><description>&lt;h4>Objective&lt;/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.&lt;h4>Data sources&lt;/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.&lt;h4>Study design&lt;/h4>We compared the bias and root mean squared error of treatment effect estimates from six model spe</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Oct</publication><modification>2025-04-04T10:21:26.129Z</modification><creation>2025-04-04T10:21:26.129Z</creation></dates><accession>S-EPMC8522571</accession><cross_references><pubmed>33978956</pubmed><doi>10.1111/1475-6773.13666</doi></cross_references></HashMap>