Rank-based inference in the proportional hazards model for interval censored data
区间删失数据比例风险模型的秩推断
A marginal likelihood approach to fitting the proportional hazards model to interval censored or grouped data is proposed; this approach maximises a likelihood that is the sum over all rankings of the data that are consistent with the observed censoring intervals. As in the usual proportional hazards model, the method does not require specification of the bascline hazard function. The score equations determining the maximum marginal likelihood estimator can be written as the…