论文标题
具有离散时间生存数据的危险概率和赔率模型的一致和稳健的推断
Consistent and robust inference in hazard probability and odds models with discrete-time survival data
论文作者
论文摘要
对于离散的时间生存数据，在理论上，Cox危险赔率模型中的条件可能性推断是理论上的，但精确的计算是数值棘手的，具有中等至大量的绑扎事件。回归系数和基线危害概率上的无条件最大似然估计可能会在大量的时间间隔中有问题。我们使用数值简单的估计函数以及基于模型的模型和模型方差估计，在危险概率和赔率模型中开发新的方法和理论。对于概率危害模型，我们得出作为一致的估计器Breslow-Peto估计器，以前称为危险率模型中条件可能性估计器的近似值。对于赔率危害模型，我们提出了一个加权的壁炉架 - 估计器，鉴于除风险集和协变量外，与有条件的可能性估计量相似，该估计值还满足有条件的无偏见。我们的方法有望在广泛的设置中令人满意地执行，其中少量或大量的绑扎事件对应于大或少量的时间间隔。这些方法是在R软件包dsurvival中实现的。
For discrete-time survival data, conditional likelihood inference in Cox's hazard odds model is theoretically desirable but exact calculation is numerical intractable with a moderate to large number of tied events. Unconditional maximum likelihood estimation over both regression coefficients and baseline hazard probabilities can be problematic with a large number of time intervals. We develop new methods and theory using numerically simple estimating functions, along with model-based and model-robust variance estimation, in hazard probability and odds models. For the probability hazard model, we derive as a consistent estimator the Breslow-Peto estimator, previously known as an approximation to the conditional likelihood estimator in the hazard odds model. For the odds hazard model, we propose a weighted Mantel-Haenszel estimator, which satisfies conditional unbiasedness given the numbers of events in addition to the risk sets and covariates, similarly to the conditional likelihood estimator. Our methods are expected to perform satisfactorily in a broad range of settings, with small or large numbers of tied events corresponding to a large or small number of time intervals. The methods are implemented in the R package dSurvival.
