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Cox 比例风险回归模型在流行病学研究中的评估。

An Assessment of the Cox Proportional Hazards Regression Model for Epidemiologic Studies.

机构信息

Center for Health Sciences, Exponent, Inc., Bellevue, WA, and Menlo Park, CA, USA.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

出版信息

Risk Anal. 2018 Apr;38(4):777-794. doi: 10.1111/risa.12865. Epub 2017 Nov 23.

Abstract

The basic assumptions of the Cox proportional hazards regression model are rarely questioned. This study addresses whether hazard ratio, i.e., relative risk (RR), estimates using the Cox model are biased when these assumptions are violated. We investigated also the dependence of RR estimates on temporal exposure characteristics, and how inadequate control for a strong, time-dependent confounder affects RRs for a modest, correlated risk factor. In a realistic cohort of 500,000 adults constructed using the National Cancer Institute Smoking History Generator, we used the Cox model with increasing control of smoking to examine the impact on RRs for smoking and a correlated covariate X. The smoking-associated RR was strongly modified by age. Pack-years of smoking did not sufficiently control for its effects; simultaneous control for effect modification by age and time-dependent cumulative exposure, exposure duration, and time since cessation improved model fit. Even then, residual confounding was evident in RR estimates for covariate X, for which spurious RRs ranged from 0.980 to 1.017 per unit increase. Use of the Cox model to control for a time-dependent strong risk factor yields unreliable RR estimates unless detailed, time-varying information is incorporated in analyses. Notwithstanding, residual confounding may bias estimated RRs for a modest risk factor.

摘要

Cox 比例风险回归模型的基本假设很少受到质疑。本研究探讨了当这些假设被违反时,使用 Cox 模型估计的风险比(即相对风险 [RR])是否存在偏差。我们还研究了 RR 估计值对时间暴露特征的依赖性,以及对一个强大的、与时间相关的混杂因素控制不足如何影响适度的、相关风险因素的 RR。我们使用国家癌症研究所吸烟史生成器构建了一个现实的 50 万成年人队列,使用 Cox 模型,随着吸烟控制的增加,检查了吸烟和相关协变量 X 的 RR 的影响。吸烟相关的 RR 受到年龄的强烈影响。吸烟量并不能充分控制其影响;同时控制年龄和时间依赖性累积暴露、暴露持续时间以及戒烟时间的效应修饰作用,改善了模型拟合度。即便如此,在协变量 X 的 RR 估计中仍存在残余混杂,其虚假 RR 范围为每单位增加 0.980 至 1.017。除非在分析中纳入详细的、随时间变化的信息,否则使用 Cox 模型控制一个与时间相关的强风险因素会导致不可靠的 RR 估计。

尽管如此,残余混杂仍可能会使适度风险因素的估计 RR 产生偏差。

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