Counter-matching in studies of gene-environment interaction: efficiency and feasibility.
The interest in studying gene-environment interaction is increasing for complex diseases. However, most methods of detecting gene-environment interactions may not be appropriate for the study of interactions involving rare genes (G:) or uncommon environmental exposures (E:), because of poor statistical power. To increase this power, the authors propose the counter-matching design. This design increases the number of subjects with the rare factor without increasing the number of measurements that must be performed. In this paper, the efficiency and feasibility (required sample sizes) of counter-matching designs are evaluated and discussed. Counter-matching on both G: and E: appears to be the most efficient design for detecting gene-environment interaction. The sensitivity and specificity of the surrogate measures, the frequencies of G: and E:, and, to a lesser extent, the value of the interaction effect are the most important parameters for determining efficiency. Feasibility is also more dependent on the exposure frequencies and the interaction effect than on the main effects of G: and E: Although the efficiency of counter-matching is greatest when the risk factors are very rare, the study of such rare factors is not realistic unless one is interested in very strong interaction effects. Nevertheless, counter-matching appears to be more appropriate than most traditional epidemiologic methods for the study of interactions involving rare factors.
Andrieu
N
N
Unité de Recherche en Epidémiologie des Cancers, Institut de la Santé et de la Recherche Médicale (INSERM) U521, Institut Gustave-Roussy, 94805 Villejuif, France. nandrieu@igr.fr
Goldstein
A M
AM
Thomas
D C
DC
Langholz
B
B
D016022:Case-Control Studies; D015331:Cohort Studies; D004777:Environment; D004812:Epidemiologic Methods; D005240:Feasibility Studies; D005823:Genetics; D006801:Humans; D016555:Matched-Pair Analysis; D008957:Models, Genetic; D012107:Research Design; D012680:Sensitivity and Specificity