Mantel-Haenszel estimators for irregular sparse K2×J tables

Abstract

This paper deals with sparse K2×J(J>2) tables. Projection-method Mantel–Haenszel (MH) estimators of the common odds ratios have been proposed for K2×J tables, which include Greenland's generalized MH estimator as a special case. The method projects log-transformed MH estimators for all K2×2 subtables, which were called naive MH estimators, onto a linear space spanned by log odds ratios. However, for sparse tables it is often the case that naive MH estimators are unable to be computed. In this paper we introduce alternative naive MH estimators using a graph that represents K2×J tables, and apply the projection to these alternative estimators. The idea leads to infinitely many reasonable estimators and we propose a method to choose the optimal one by solving a quadratic optimization problem induced by the graph, where some graph-theoretic arguments play important roles to simplify the optimization problem. An illustration is given using data from a case–control study. A simulation study is also conducted, which indicates that the MH estimator tends to have a smaller mean squared error than the MH estimator previously suggested and the conditional maximum likelihood estimator for sparse tables.