Missing Data Analysis: A Case Study of a Randomized Controlled Trial

Simple item page
dc.contributor.advisorZhang, Guangyuen_US
dc.contributor.authorPatzer, Shaleah Mary Murphyen_US
dc.contributor.departmentPublic and Community Healthen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2009-07-03T05:46:59Z
dc.date.available2009-07-03T05:46:59Z
dc.date.issued2009en_US
dc.description.abstractMissing data is a pervasive problem in the analysis of many clinical trials. In order for the analysis of a study to produce unbiased estimators, the missing data problem must be addressed. First, the missing data pattern must be established; second, the missingness mechanism must be determined; and third, the most appropriate imputation method for imputing the missing values must be found. The purpose of this paper is to explore the imputation methods best suited for the missing data from the Diet and Exercise for Elevated Risk Trial (DEER) in a secondary analysis of the data. The missingness pattern in the data set is arbitrary and the missingness mechanism is MAR. A simulation study suggests that the two best methods for imputation are subject-specific mean imputation and multiple imputation. I conclude that mean imputation is the best method for handling missing data in the DEER data set.en_US
dc.format.extent210154 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/9366
dc.language.isoen_US
dc.subject.pqcontrolledHealth Sciences, Epidemiologyen_US
dc.subject.pquncontrolledarbitrary missingness patternen_US
dc.subject.pquncontrolledmissing data analysisen_US
dc.titleMissing Data Analysis: A Case Study of a Randomized Controlled Trialen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Patzer_umd_0117N_10421.pdf
Size:
205.23 KB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Epidemiology & Biostatistics Theses and Dissertations