Nonparametric models and methods for designs with dependent censored data:Part I

We consider a nonparametric (NP) approach to the analysis of repeated measu res designs with censored data. Using the NP model of Akritas and Arnold (1 994, Journal of the American Statistical Association 89, 336-343) for margi nal distributions, we present test procedures for the NP hypotheses of no m ain effects, no interaction, and no simple effects. This extends the existi ng NP methodology for such designs (Wei and Lachin, 1984, Journal of the Am erican Statistical Association 79, 653-661). The procedures do not require any modeling assumptions and should be useful in cases where the assumption s of proportional hazards or location shift fail to be satisfied. The large -sample distribution of the test statistics is based on an i.i.d. represent ation for Kaplan-Meier integrals. The testing procedures apply also to ordi nal data and to data with ties. Useful small-sample approximations are pres ented, and their performance is examined in a simulation study. Finally, th e methodology is illustrated with two real life examples, one with censored and one with missing data. It is indicated that one of the data sets does not conform to any set of assumptions underlying the available methods and also that the present method provides a useful additional analysis even whe n data sets conform to modeling assumptions.