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IJE Advance Access originally published online on March 23, 2006
International Journal of Epidemiology 2006 35(4):1067-1073; doi:10.1093/ije/dyl048
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Published by Oxford University Press

Methodology

Unconditional analyses can increase efficiencyin assessing gene–environment interaction of the case-combined-control design

Alisa M Goldstein1,*, Marie-Gabrielle Dondon2 and Nadine Andrieu3

1 Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Bethesda, MD 20892, USA
2 Inserm, IC10213, Service de Biostatistiques, Institut Curie, 26 rue d'Ulm, 75248 Paris Cedex 5, France
3 Inserm, EMI00-06, Service de Biostatistiques, Institut Curie, 26 rue d'Ulm, 75248 Paris Cedex 5, France

* Corresponding author. Genetic Epidemiology Branch/NCI/NIH/DHHS, Executive Plaza South, Room 7004, 6120 Executive Blvd, MSC 7236, Bethesda, MD 20892-7236, USA. E-mail: goldstea{at}exchange.nih.gov

Background A design combining both related and unrelated controls, named the case-combined-control design, was recently proposed to increase the power for detecting gene–environment (GxE) interaction. Under a conditional analytic approach, the case-combined-control design appeared to be more efficient and feasible than a classical case–control study for detecting interaction involving rare events.

Methods We now propose an unconditional analytic strategy to further increase the power for detecting gene–environment (GxE) interactions. This strategy allows the estimation of GxE interaction and exposure (E) main effects under certain assumptions (e.g. no correlation in E between siblings and the same exposure frequency in both control groups). Only the genetic (G) main effect cannot be estimated because it is biased.

Results Using simulations, we show that unconditional logistic regression analysis is often more efficient than conditional analysis for detecting GxE interaction, particularly for a rare gene and strong effects. The unconditional analysis is also at least as efficient as the conditional analysis when the gene is common and the main and joint effects of E and G are small.

Conclusions Under the required assumptions, the unconditional analysis retains more information than does the conditional analysis for which only discordant case–control pairs are informative leading to more precise estimates of the odds ratios.

Keywords GxE interaction, unconditional logistic regression analysis, conditional analysis, sibling controls, population-based controls

Accepted 2 March 2006


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