Nonparametric models for clustered data: Application to root survival study
Michael G. Akritas, Yunling E. Du, David M. Eissenstat · Journal of nonparametric statistics · 2004
Root survival data are discrete, have censoring and there is dependence due to clustering. The assumptions of the common semiparametric models seem to be violated for the data set under consideration. A fully nonparametric model for such clustered data is proposed and methods for testing hypotheses about the main effects and interaction are developed. Simulation studies indicate that the procedures have reasonable accuracy for moderate sample sizes. For small sample sizes, bootstrapping from the permutation distribution performs rather well for testing simple effects. The nonparametric analysis of the data set is compared with that based on semiparametric models, and the effect of allowing for the dependence caused by the clustering is examined.