Interval estimation of common risk difference for stratified unilateral and bilateral data✩.

In clinical trials, unilateral or bilateral data can usually be encountered if a subject contributes one or both of paired organs. For the bilateral data, responses from two paired body parts are correlated. In this paper, we study various confidence intervals of common risk difference in stratified unilateral and bilateral data based on the Dallal's model. Simulation results show that the score method outperforms other methods and provides coverage probability close to the nominal level and satisfactory coverage width. Hence, the method is recommended. In addition, the inverse hyperbolic tangent Wald-type become as optimal as the score method with the increase of sample sizes. An otolaryngology example is used to demonstrate the proposed methods.

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

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 S C 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 S C is recommended according to its robust TIEs and satisfactory power.

Homogeneity Test of the First-Order Agreement Coefficient in a Stratified Design.

Gwet's first-order agreement coefficient (AC1) is widely used to assess the agreement between raters. This paper proposes several asymptotic statistics for a homogeneity test of stratified AC1 in large sample sizes. These statistics may have unsatisfactory performance, especially for small samples and a high value of AC1. Furthermore, we propose three exact methods for small pieces. A likelihood ratio statistic is recommended in large sample sizes based on the numerical results. The exact E approaches under likelihood ratio and score statistics are more robust in the case of small sample scenarios. Moreover, the exact E method is effective to a high value of AC1. We apply two real examples to illustrate the proposed methods.