On the choice of time scales in competing risks predictions
On the choice of time scales in competing risks predictions
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DOI：
10.1093/biostatistics/kxw024
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发表时间：
2017-01-01
期刊：
影响因子：
2.1
通讯作者：
Fine, Jason P.
中科院分区：
文献类型：
--
作者：
Lee, Minjung;Gouskova, Natalia A.;Fine, Jason P.
在竞争风险数据的标准分析中，比例风险模型适用于同一时间尺度上所有原因的特定原因风险函数。这些回归分析是基于结合估计的特定原因危害函数预测特定原因累积发生率函数的基础。然而，在疾病登记处产生的预测中，只有患有疾病的受试者进入数据库，疾病相关死亡率可能更自然地建模自诊断时间尺度的时间，而其他原因导致的死亡可能更自然地建模年龄时间尺度。如果对其中一个原因采用了不正确的时间尺度，并且没有替代方法，则单一时间尺度方法可能会有偏差。我们提出的累积发病率函数，其中回归模型的原因特定的风险函数可以指定在不同的时间尺度上的推断。使用疾病登记数据，分析年龄范围内的其他原因死亡率需要在疾病诊断年龄时截断事件时间，使分析复杂化。此外，标准鞅理论不适用于组合不同时间尺度上的回归模型。我们建立协变量的条件预测是一致的，渐近正态使用经验过程技术，并提出一致的方差估计构建置信区间。仿真研究表明，所提出的两个时间尺度的方法表现良好，优于单时间尺度的预测时，时间尺度是错误的。这些方法用从美国国家癌症研究所的监测、流行病学和最终结果项目中获得的III期结肠癌数据来说明。
In the standard analysis of competing risks data, proportional hazards models are fit to the cause-specific hazard functions for all causes on the same time scale. These regression analyses are the foundation for predictions of cause-specific cumulative incidence functions based on combining the estimated cause-specific hazard functions. However, in predictions arising from disease registries, where only subjects with disease enter the database, disease-related mortality may be more naturally modeled on the time since diagnosis time scale while death from other causes may be more naturally modeled on the age time scale. The single time scale methodology may be biased if an incorrect time scale is employed for one of the causes and an alternative methodology is not available. We propose inferences for the cumulative incidence function in which regression models for the cause-specific hazard functions may be specified on different time scales. Using the disease registry data, the analysis of other cause mortality on the age scale requires left truncating the event time at the age of disease diagnosis, complicating the analysis. In addition, standard Martingale theory is not applicable when combining regression models on different time scales. We establish that the covariate conditional predictions are consistent and asymptotically normal using empirical process techniques and propose consistent variance estimators for constructing confidence intervals. Simulation studies show that the proposed two time scales method performs well, outperforming the single time-scale predictions when the time scale is misspecified. The methods are illustrated with stage III colon cancer data obtained from the Surveillance, Epidemiology, and End Results program of National Cancer Institute.