Group for Research in Decision Analysis

Nonparametric regression under biased sampling and informative censoring for parametric truncation model

Yassir Rabhi Université de Sherbrooke, Canada

In observational studies, incidence cohort sampling is ideally adopted to study individuals, who have not experienced a disease, from disease onset to a failure event. Logistic or other constraints (rare disease, cost of study) may, however, preclude the possibility of recruiting incident cases. A feasible alternative in such circumstances is to sample subjects who have already experienced the onset of a disease, through cross-sectional sampling.

In this presentation, we discuss the nonparametric estimation of the regression function \(m(x) = E[ Y| X = x]\), under the model \(Y = m(X) + \epsilon \), when the data \((Y, X\)) is subject to biased selection and random censoring. We introduce a methodology for known parametric forms of the left-truncation distribution. In the length-biased case, our method show efficiency as compared to the one of Iglesias-Perez & Gonzalez-Manteiga (1999). The proposed method is then applied to analyze two data sets on the mortality of patients with AIDS and the survival of elderly individuals with dementia.

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