Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms

• Main Authors: Keyi Mou, Zhiming Li
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Complexity
• Online Access: http://dx.doi.org/10.1155/2021/6685951

In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based on optimal algorithms. Monte Carlo simulations show the performance of these algorithms through the total averaged estimation error, SD, MSE, and convergence rate. Score statistic is more robust and has satisfactory power. Two real examples are given to illustrate our proposed methods.