Resumen de Inferences for odds ratio with dependent pairs
Yinshan Zhao, Harry Joe
In familial data analyses quantifying familial association is scientifically important. As analogies of the intraclass and interclass correlations of a normally distributed trait, we study intraclass and interclass (log) odds ratios for a binary trait. We propose non-parametric estimators of the odds ratios and derive the asymptotic variances of the estimators under the assumptions of exchangeability and closure of multivariate binary distributions under marginals. These estimators are straightforward, except for the consideration of how to weight by family size. The relative efficiencies of the non-parametric estimators are studied for some parametric models. It shows that our estimators are highly efficient, and that weighting by family size is recommended for the intraclass odds ratio. The computations of the estimators and their standard errors are illustrated with two examples.
