title: A simple test of the homogeneity of risk difference in sparse data: an application to a multicenter study.
abstract: In this paper, we focus discussion on testing the homogeneity of risk difference for sparse data, in which we have few patients in each stratum, but a moderate or large number of strata. When the number of patients per treatment within strata is small (2 to 5 patients), none of test procedures proposed previously for testing the homogeneity of risk difference for sparse data can really perform well. On the basis of bootstrap methods, we develop a simple test procedure that can improve the power of the previous test procedures. Using Monte Carlo simulations, we demonstrate that the test procedure developed here can perform reasonable well with respect to Type I error even when the number of patients per stratum for each treatment is as small as two patients. We evaluate and study the power of the proposed test procedure in a variety of situations. We also include a comparison of the performance between the test statistics proposed elsewhere and the test procedure developed here. Finally, we briefly discuss the limitation of using the proposed test procedure. We use the data comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the proposed test procedure.
authors.affiliation: Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182-7720, USA. kjl@rohan.sdsu.edu
authors.forename: Kung-Jong
authors.initials: KJ
authors.lastname: Lui
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007938:Leukemia / Q000188:drug therapy / Q000453:epidemiology; D009010:Monte Carlo Method; D015337:Multicenter Studies as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D009369:Neoplasms / Q000188:drug therapy / Q000453:epidemiology; D016012:Poisson Distribution; D011237:Predictive Value of Tests; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D012306:Risk; D015983:Selection Bias; D016019:Survival Analysis