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In an observational setting, treatment assignment at each clinical visit follows a decision strategy where the treating clinician selects treatment based on current and past clinical measurements as well as treatment histories. These time-dependent structures, coupled with inherent correlations between and within each visit, add on to the data complexity. Despite recent interest in Bayesian causal methods, only a limited literature has explored approaches to handle longitudinal data and no method handles repeatedly measured outcomes. In this paper, we extended two Bayesian approaches: Bayesian estimation of marginal structural models and two-stage Bayesian propensity score analysis to handle a repeatedly measured outcome. Our proposed methods permit causal estimation of treatment effects at each visit. Time-dependent inverse probability of treatment weights are obtained from the Markov chain Monte Carlo samples of the posterior treatment assignment model for each follow-up visit. We use a simulation study to validate and compare the proposed methods and illustrate our approaches through a study of intravenous immunoglobulin therapy in treating newly diagnosed juvenile dermatomyositis. <\/jats:p>","DOI":"10.1177\/0962280219900362","type":"journal-article","created":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T10:34:13Z","timestamp":1580294053000},"page":"2507-2519","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Estimation of causal effects with repeatedly measured outcomes in a Bayesian framework"],"prefix":"10.1177","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5017-1276","authenticated-orcid":false,"given":"Kuan","family":"Liu","sequence":"first","affiliation":[{"name":"Dalla Lana School of Public Health, University of Toronto, Toronto, Canada"},{"name":"Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, 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