Test homogeneity of risk difference across subgroups in clinical trials.
A weighted least squares statistic is commonly used to test homogeneity of the risk difference for a series of 2 x 2 tables. Since the method is based on asymptotic theory, its type I error rate is inflated when the data are sparse. Two new methods for testing the homogeneity of risk difference across different groups in clinical trials are proposed in this paper. These methods are constructed, based on the Wilson's score test and traditional weighted least squares statistics. The performance of the new methods is evaluated and compared to the currently available approaches. Results show that one of our new methods has a type I error rate that is closest to the nominal level among all the methods and is much more powerful than those proposed by Lipsitz et al.
Zhang
Lanju
L
Department of Biostatistics, MedImmune LLC, One MedImmune Way, Gaithersburg, Maryland 20878, USA. zhangla@medimmune.com
Yang
Harry
H
Cho
Iksung
I
D000465:Algorithms; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009101:Multiple Myeloma / Q000188:drug therapy; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000706:statistics & numerical data; D016019:Survival Analysis; D016896:Treatment Outcome