Introduction to propensity score methods

dc.contributor.advisorSõnajalg, Jaak, juhendaja
dc.contributor.advisorFischer, Krista, juhendaja
dc.contributor.authorJesse, Kristin
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Matemaatika ja statistika instituutet
dc.date.accessioned2021-07-01T09:14:41Z
dc.date.available2021-07-01T09:14:41Z
dc.date.issued2021
dc.description.abstractRandomised controlled trials (RCTs), while the golden standard of estimating causal effects in clinical studies, are not always possible to conduct due to ethical reasons or other restrictions. Observational studies are an alternative in such cases. However, in such studies, treatment assignment may be subject to systematic biases. Propensity score (PS) methods are a popular tool to adjust for confounding factors in observational studies. By attempting to mimic RCTs, these methods are quite intuitive. This thesis provides a theoretical overview of the most popular PS methods, and conducts a simulation study to compare PS matching, PS weighting, and conventional covariate adjustment. CERCS research specialisation: P160 Statistics, operation research,en
dc.identifier.urihttp://hdl.handle.net/10062/72892
dc.language.isoenget
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectkalduvuse määret
dc.subjectvaatlusuuringudet
dc.subjectühildamineet
dc.subjectkaalumineet
dc.subjecttunnustele kohandamineet
dc.subjectcovariate adjustmenten
dc.subjectweightingen
dc.subjectmatchingen
dc.subjectobservational studiesen
dc.subjectpropensity scoreen
dc.titleIntroduction to propensity score methodsen
dc.typeinfo:eu-repo/semantics/masterThesiset

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