uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Nonparametric Combination Methodology: A Better Way to Handle Composite Endpoints?
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Composite endpoints are widely used in clinical trials. The outcome of a clinical trial can affect many individuals and it is therefore of importance that the methods used are as effective and correct as possible. Improvements of the standard method of testing composite endpoints have been proposed and in this thesis, the alternative method using nonparametric combination methodology is compared to the standard method. Performing a simulation study, the power of three combining functions (Fisher, Tippett and the Logistic) are compared to the power of the standard method. The performances of the four methods are evaluated for different compositions of treatment effects, as well as for independent and dependent components. The results show that using the nonparametric combination methodology leads to higher power in both dependent and independent cases. The combining functions are suitable for different compositions of treatment effects, the Fisher combining function being the most versatile. The thesis is written with support from Statisticon AB.

Place, publisher, year, edition, pages
2015. , 38 p.
Keyword [en]
composite endpoints, multivariate randomization tests, combining p-values, Fisher combining function, Tippett combining function, logistic combining function
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-274959OAI: oai:DiVA.org:uu-274959DiVA: diva2:897930
External cooperation
Statisticon AB
Subject / course
Statistics
Supervisors
Examiners
Available from: 2016-02-22 Created: 2016-01-26 Last updated: 2016-02-22Bibliographically approved

Open Access in DiVA

fulltext(916 kB)96 downloads
File information
File name FULLTEXT01.pdfFile size 916 kBChecksum SHA-512
085ca3fc0fa74098ec948f281de1b8238fafab42f194c4f94f2c4f9cd4cd98162b83dd0d30b5c66b620f30c5e9fe3e9e1c057158f7f3c469b0ec0d3e989d25a8
Type fulltextMimetype application/pdf

By organisation
Department of Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 96 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 267 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf