Relative risk regression for current status data in case-cohort studies
We propose using the weighted likelihood method to fit a general relative risk regression model for the current status data with missing data as arise, for example, in case-cohort studies. The missingness probability is either known or can be reasonably estimated. Asymptotic properties of the weighted likelihood estimators are established. For the case of using estimated weights, we construct a general theorem that guarantees the asymptotic normality of the M-estimator of a finite dimensional parameter in a class of semiparametric models, where the infinite dimensional parameter is allowed to converge at a slower than parametric rate, and some other parameters in the objective function are estimated a priori. The weighted bootstrap method is employed to estimate the variances. Simulations show that the proposed method works well for finite sample sizes. A motivating example of the case-cohort study from an HIV vaccine trial is used to demonstrate the proposed method. © 2011 Statistical Society of Canada.
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