ABSTRACT Double‐censored data can occur in many areas like clinical trials, epidemiology, cancer research and survival analysis. Such data usual refers failure time of interest can be observed only if it is within an observation interval or a window, encompassing instances of left censoring (events occurring before observation begins) and right censoring (events occurring after observation ends). Many methods have been proposed for their analyses and most of the existing methods assume that or apply only to the situation where the censoring is independent. However, it is well‐known this may not be true or one may face informative censoring in many cases. To address this, we proposed a generalized accelerated hazards frailty model that allows for dependent censoring among other advantages and includes the proportional hazards, accelerated failure time and traditional accelerated hazards frailty models as special cases. For inference, we propose a joint model‐based sieve maximum likelihood approach and develop an EM‐based algorithm for its implementation. Also the profile approach is adapted for variance estimation and the proposed estimator of regression parameters is shown to be consistent and asymptotically normal. Furthermore an extensive simulation study is performed and suggests that the proposed method works well in practical situations and it is applied to a set of real data from an AIDS clinical trial that motivated this study.
Ma et al. (Sun,) studied this question.
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