Nonparametric Models and Methods for Designs with Dependent Censored Data: Part I

John T. O'Gorman and Michael G. Akritas

Biometrics, 2001, vol. 57, issue 1, 88-95

Abstract: Summary. We consider a nonparametric (NP) approach to the analysis of repeated measures designs with censored data. Using the NP model of Akritas and Arnold (1994, Journal of the American Statistical Association89, 336–343) for marginal distributions, we present test procedures for the NP hypotheses of no main effects, no interaction, and no simple effects. This extends the existing NP methodology for such designs (Wei and Lachin, 1984, Journal of the American Statistical Association79, 653‐661). The procedures do not require any modeling assumptions and should be useful in cases where the assumptions 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. representation for Kaplan–Meier integrals. The testing procedures apply also to ordinal data and to data with ties. Useful small‐sample approximations are presented, and their performance is examined in a simulation study. Finally, the 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 when data sets conform to modeling assumptions.

Date: 2001
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