Statistical analysis of molecular epidemiology studies employing case-series Journal Article


Authors: Begg, C. B.; Zhang, Z. F.
Article Title: Statistical analysis of molecular epidemiology studies employing case-series
Abstract: The case-series design is being used increasingly to explore associations between environmental risk factors and genetic markers. It is demonstrated that the odds ratio derived from a case-series study is the ratio of the relative risk for developing marker-positive disease to the relative risk for developing marker-negative disease. This parameter is an empirical manifestation of etiological heterogeneity with respect to the risk factor under study, and it can be used to construct a statistical significance test. Presence of etiological heterogeneity, as reflected in departures of this parameter from unity, could be a result of either the presence of distinct causal mechanisms for the two categories of cases, or a different strength of effect via the same mechanism. The case-series approach represents an efficient and valid approach for evaluating gene-environment associations, especially in referral centers where it is difficult to identify a valid control group. © 1994, American Association for Cancer Research. All rights reserved.
Keywords: controlled study; gene mutation; human cell; mutation; case-control studies; tumor markers, biological; odds ratio; risk factors; smoking; protein p53; risk factor; confidence intervals; cancer genetics; statistical analysis; models, genetic; bladder neoplasms; cancer epidemiology; mathematical computing; environmental exposure; genetic marker; genetic markers; genetic heterogeneity; occupational exposure; human; priority journal; article; support, u.s. gov't, p.h.s.; proto-oncogene protein p21(ras)
Journal Title: Cancer Epidemiology Biomarkers and Prevention
Volume: 3
Issue: 2
ISSN: 1055-9965
Publisher: American Association for Cancer Research  
Date Published: 1994-03-01
Start Page: 173
End Page: 175
Language: English
PROVIDER: scopus
PUBMED: 8049640
DOI/URL:
Notes: Source: Scopus
Citation Impact
MSK Authors
  1. Colin B Begg
    306 Begg
  2. Zuo-Feng Zhang
    102 Zhang