Adjusting O'Brien's test to control type I error for the generalized nonparametric Behrens-Fisher problem.
O'Brien (1984, Biometrics 40, 1079-1087) introduced a simple nonparametric test procedure for testing whether multiple outcomes in one treatment group have consistently larger values than outcomes in the other treatment group. We first explore the theoretical properties of O'Brien's test. We then extend it to the general nonparametric Behrens-Fisher hypothesis problem when no assumption is made regarding the shape of the distributions. We provide conditions when O'Brien's test controls its error probability asymptotically and when it fails. We also provide adjusted tests when the conditions do not hold. Throughout this article, we do not assume that all outcomes are continuous. Simulations are performed to compare the adjusted tests to O'Brien's test. The difference is also illustrated using data from a Parkinson's disease clinical trial.
Huang
Peng
P
Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, South Carolina 29425, USA. huangp@musc.edu
Tilley
Barbara C
BC
Woolson
Robert F
RF
Lipsitz
Stuart
S
D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D015999:Multivariate Analysis; D010300:Parkinson Disease / Q000188:drug therapy; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D018709:Statistics, Nonparametric; D016019:Survival Analysis; D016896:Treatment Outcome; D014451:Ubiquinone / Q000627:therapeutic use
D014451:Ubiquinone