Doubly Robust Censoring Unbiased Transformations
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Abstract:
We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. Additionally, we discuss how doubly robust censoring unbiased transformations can be utilized for regression with missing data, in causal inference problems, or with current status data
Subject Area:
Statistical Theory and Methods, Survival Analysis
Suggested Citation:
Daniel Rubin and Mark J. van der Laan, "Doubly Robust Censoring Unbiased Transformations" (June 2006). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 208.
http://www.bepress.com/ucbbiostat/paper208