Homogeneity test of relative risk ratios for stratified bilateral data under different algorithms

Abstract

Medical clinical studies about paired body parts often involve stratified bilateral data. The correlation between responses from paired parts should be taken into account to avoid biased or misleading results. This paper aims to test if the relative risk ratios across strata are equal under the optimal algorithms. Based on different algorithms, we obtain the desired global and constrained maximum likelihood estimations (MLEs). Three asymptotic test statistics (i.e. $T_L$ , $T_{SC}$ and $T_W$ ) are proposed. Monte Carlo simulations are conducted to evaluate the performance of these algorithms with respect to mean square errors of MLEs and convergence rate. The empirical results show Fisher scoring algorithm is usually better than other methods since it has effective convergence rate for global MLEs, and makes mean-square error lower for constrained MLEs. Three test statistics are compared in terms of type I error rate (TIE) and power. Among these statistics, $T_{SC}$ is recommended according to its robust TIEs and satisfactory power.

Keywords: Bilateral data; Donner's model; Type I error rate; algorithm; asymptotic test; power.