论文标题
有效估计添加剂风险模型的间隔数据
Efficient Estimation of the Additive Risks Model for Interval-Censored Data
论文作者
论文摘要
与流行的COX模型相反，该模型在事件危害的时间内提出了多种协变量效应规范，半参数添加剂风险模型（ARM）提供了有吸引力的添加剂规范，从而可以直接评估变化或危害函数的危害函数的变化价值的变化值。该手臂是一个灵活的模型，可以估计与时间无关和时变的协变量。它具有通过有限维参数确定的非参数组件和回归成分。本章通过针对病例-II间隔进行的数据，对模型的非参数和有限维成分的最大可能性（ML）估计提出了有效的方法。我们提出的MM方法的工作特性通过模拟研究评估，并通过R套件Mmintadd在乳腺癌数据集上进行了说明。预计所提出的计算方法不仅将为ML估计方案提供可伸缩性，而且还可以简化其他复杂可能性或模型的计算负担。
In contrast to the popular Cox model which presents a multiplicative covariate effect specification on the time to event hazards, the semiparametric additive risks model (ARM) offers an attractive additive specification, allowing for direct assessment of the changes or the differences in the hazard function for changing value of the covariates. The ARM is a flexible model, allowing the estimation of both time-independent and time-varying covariates. It has a nonparametric component and a regression component identified by a finite-dimensional parameter. This chapter presents an efficient approach for maximum-likelihood (ML) estimation of the nonparametric and the finite-dimensional components of the model via the minorize-maximize (MM) algorithm for case-II interval-censored data. The operating characteristics of our proposed MM approach are assessed via simulation studies, with illustration on a breast cancer dataset via the R package MMIntAdd. It is expected that the proposed computational approach will not only provide scalability to the ML estimation scenario but may also simplify the computational burden of other complex likelihoods or models.
