umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • 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
Statistical power considerations in genotype-based recall randomized controlled trials
Show others and affiliations
2016 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 6, 37307Article in journal (Refereed) Published
Abstract [en]

Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for genemetformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.

Place, publisher, year, edition, pages
2016. Vol. 6, 37307
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:umu:diva-128954DOI: 10.1038/srep37307ISI: 000388480300001PubMedID: 27886175OAI: oai:DiVA.org:umu-128954DiVA: diva2:1061374
Available from: 2017-01-02 Created: 2016-12-20 Last updated: 2017-01-10Bibliographically approved

Open Access in DiVA

fulltext(1567 kB)23 downloads
File information
File name FULLTEXT01.pdfFile size 1567 kBChecksum SHA-512
d05eeac28e2c1def615173906f6872c4307ae1ff4346effb2f53d63f207d5a28c8fe991b2af38f70449cf2fbca57de89ce1f02a51e801a1a4bcc58eeac542e25
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Franks, Paul W.
By organisation
Department of Public Health and Clinical Medicine
In the same journal
Scientific Reports
Endocrinology and Diabetes

Search outside of DiVA

GoogleGoogle Scholar
Total: 23 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

Altmetric score

Total: 47 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • 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