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Profile Matching in Observational Studies with Multilevel Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Profile Matching in Observational Studies with Multilevel Data./
Author:	McGrath, Brenda.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	143 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
Subject:	Statistics. -
ISBN:	9798834038931

Profile Matching in Observational Studies with Multilevel Data.
McGrath, Brenda.

Profile Matching in Observational Studies with Multilevel Data. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 143 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--North Dakota State University, 2022.

This item must not be sold to any third party vendors.

Matching 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.

ISBN: 9798834038931Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Profile matching

Profile Matching in Observational Studies with Multilevel Data.
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100 1 $a McGrath, Brenda. $0 (orcid)0000-0003-4208-1059 $3 3689198
245 1 0 $a Profile Matching in Observational Studies with Multilevel Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 143 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
500 $a Advisor: Choi, Bong-Jin.
502 $a Thesis (Ph.D.)--North Dakota State University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Matching 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.
590 $a School code: 0157.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
653 $a Profile matching
653 $a Observational data
653 $a Mathematical optimization
690 $a 0463
690 $a 0308
710 2 0 $a North Dakota State University. $b Statistics. $3 2095058
773 0 $t Dissertations Abstracts International $g 84-01B.
790 $a 0157
791 $a Ph.D.
792 $a 2022
793 $a English