Document Detail


Sequential tests for gene-environment interactions in matched case-control studies.
MedLine Citation:
PMID:  15580604     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The sample size necessary to detect a significant gene x environment interaction in an observational study can be large. For reasons of cost-effectiveness and efficient use of available biological samples we investigated the properties of sequential designs in matched case-control studies to test for both non-hierarchical and hierarchical interactions. We derived the test statistics Z and V and their characteristics when applied in a two-sided triangular test. Results of simulations show good agreement with theoretical values for V and the type I error. Power values were larger than their theoretical values for very large sample sizes. Median gain in efficiency was about 27 per cent. For a 'rare' phenotype gain in efficiency was larger when the alternative hypothesis was true than under the null hypothesis. Sequential designs lead to substantial efficiency gains in tests for interaction in matched case-control studies.
Authors:
Ingeborg van der Tweel; Maria Schipper
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  23     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2004 Dec 
Date Detail:
Created Date:  2004-12-27     Completed Date:  2005-04-05     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  3755-71     Citation Subset:  IM    
Copyright Information:
2004 John Wiley & Sons, Ltd.
Affiliation:
Centre for Biostatistics, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands. i.vandertweel@bio.uu.nl
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MeSH Terms
Descriptor/Qualifier:
Breast Neoplasms / etiology,  genetics
Case-Control Studies*
Computer Simulation
Environmental Exposure / adverse effects*
Female
Genetic Predisposition to Disease*
Humans
Models, Biological
Models, Genetic
Models, Statistical*
Research Design
Sample Size

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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