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dc.contributor.authorMcGrath, Brenda
dc.description.abstractMatching is a popular method to use with observational data to replicate desired features of a randomized control trial. A common problem encountered in observational studies is the lack of common support or the limited overlap of the covariate distributions across treatment groups. A new approach, cardinality matching, leverages mathematical optimization to directly balance observed covariates. When conducting cardinality matching, the user specifies the tolerable balance constraints of individual covariates and the desired number of matched controls. The algorithm then finds the largest possible match given these constraints. Profile matching is a newly proposed method that uses cardinality matching, in which the user can specify a target profile directly and find the largest cardinality match that is balanced to the target profile. We developed an R package called ProfileMatchit that will employ profile matching. We employed the new package in the setting of hospital quality assessment using a real-world dataset. Profile matching has not yet been used in hospital quality assessment but may be an improvement over current approaches, which have limitations in the ability to find sufficient matches in a heterogeneous sample. This application would be the culmination of our work to develop an improved version of cardinality matching and provide a new application of profile matching and a better approach to hospital quality assessment.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU policy 190.6.2en_US
dc.titleProfile Matching in Observational Studies With Multilevel Dataen_US
dc.typeThesisen_US
dc.date.accessioned2023-12-20T20:00:56Z
dc.date.available2023-12-20T20:00:56Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/10365/33413
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdfen_US
ndsu.degreeDoctor of Philosophy (PhD)en_US
ndsu.collegeScience and Mathematicsen_US
ndsu.departmentStatisticsen_US
ndsu.programStatisticsen_US
ndsu.advisorChoi, Bong-Jin


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