A COPULA-MODEL BASED SEMIPARAMETRIC INTERACTION TEST UNDER THE CASE-CONTROL DESIGN.

ABSTRACT: It is important to study the interaction between two risk factors in molecular epidemiology studies. To improve the power for the detection of interaction, some statistical testing procedures have been proposed in the literature by incorporating certain assumptions on the underlying joint distribution of the two risk factors. For example, the well known case-only test used in genetic epidemiology studies is derived under the assumption of independency between the two considered risk factors. However, those testing procedures could have detrimental effects on both false positive and false negative rates when the assumptions are not met. We propose to use a parametric copula function to model the joint distribution while leaving the marginal distributions for the two risk factors totally uns

SUBMITTER: Zhang H 

PROVIDER: S-EPMC8414873 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature