比例危险模型中的倾向加权加调整不具有双重稳健性。

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2024-07-01 DOI:10.1093/biomtc/ujae069
Erin E Gabriel, Michael C Sachs, Ingeborg Waernbaum, Els Goetghebeur, Paul F Blanche, Stijn Vansteelandt, Arvid Sjölander, Thomas Scheike
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引用次数: 0

摘要

近来,应用研究普遍将常用的生存分析建模方法(如多变量 Cox 模型和倾向得分加权法)结合起来,目的是形成一个双重稳健的暴露效应危险比估计值,当 Cox 模型或倾向得分模型被正确指定时,该估计值在大样本中是无偏的。一般来说,当确实存在因果效应时,即使经过回归标准化处理,这种组合也不会产生双重稳健估计值。我们通过模拟证明了半参数 Cox 模型、Weibull 比例危险模型和简单比例危险灵活参数模型缺乏双重稳健性,后两种模型都是通过最大似然法拟合的。我们提供了一个新颖的证明,即如果倾向得分或结果模型指定正确且包含所有混杂因素,那么倾向得分加权与比例危险生存模型的组合,无论是通过完全似然法还是部分似然法拟合,在暴露对结果无因果效应的空值下,在特定的删减机制下都是一致的。鉴于我们的研究结果表明双重稳健性只存在于空值条件下,我们概述了 2 种简单的替代估计方法,它们对给定时间点上的生存率差异具有双重稳健性(在上述意义上),前提是能够正确地对剔除机制进行建模;我们还概述了一种对完整生存率曲线具有双重稳健性的估计方法。我们在辅助信息中提供了使用这些估计器进行估计和推断的 R 代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Propensity weighting plus adjustment in proportional hazards model is not doubly robust.

Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline 2 simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.

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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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