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606 – Data Collection Using Responsive Designs and Mixed Modes
Estimation in Partially Linear Single-Index Additive Hazards Models with Current Status Data
Pooneh Pordeli
University of Calgary
Xuewen Lu
University of Calgary
Murray Burke
University of Calgary
Peter X. K. Song
University of Michigan
Current status data arise in such areas as demography, economics, epidemiology and survival models. We propose a partially linear single-index additive hazards regression model for current status data. The proposed model can model both linear and nonlinear covariate effects on the hazard and it is a parsimonious model, since it does not use too many parameters. This is particularly important for high dimensional data, which might suffer from "the curse of dimensionality". Our model reduces the dimension of the data through a single-index term. For the estimation, we use B-splines to model the unknown cumulative baseline hazard function and the nonparametric covariate functions. Asymptotic properties of the estimators are derived using the theory of counting processes. Simulation studies are presented to evaluate our method. A renal recovery data set is analyzed to illustrate the usefulness of our proposed model.