Marginal structural models to estimate the joint causal effect of nonrandomized treatments
Marginal structural models to estimate the joint causal effect of nonrandomized treatments
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DOI：
10.1198/016214501753168154
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发表时间：
2001-06-01
影响因子：
3.7
通讯作者：
Robins, JM
中科院分区：
文献类型：
--
作者：
Hernán, MA;Brumback, B;Robins, JM
即使在没有未测量的混杂因素或模型错误指定的情况下，当（a）存在时间依赖性生存风险因素也可预测后续治疗，（B）既往治疗史可预测后续风险因素水平时，用于估计时变治疗对生存率因果效应的标准方法也存在偏倚。相比之下，基于边际结构模型（MSM）的方法可以提供一致的因果关系的估计时，未测量的混杂和模型误设。MSM是一类新的因果模型，它的参数是用一类新的估计量--处理逆概率加权估计量来估计的。我们使用边际结构考克斯比例风险模型来估计齐多夫定（AZT）和预防性治疗卡氏肺孢子虫肺炎对HIV阳性男性生存率的联合作用，在多中心艾滋病队列研究中，一项对同性恋男性的观察性研究。我们获得了估计的因果死亡率（风险）比为0.67（保守的95%置信区间0.46 - 0.98）的AZT和1.14（0.79，1.64）的预防性治疗。当为我们的模型选择的函数形式是正确的，并且已经获得了预测后续治疗和死亡率的所有时间无关和时间依赖性协变量的数据时，这些估计值对于真实因果率比是一致的。
Even in the absence of unmeasured confounding factors or model misspecification, standard methods for estimating the causal effect of time-varying treatments on survival are biased when (a) there exists a time-dependent risk factor for survival that also predicts subsequent treatment, and (b) past treatment history predicts subsequent risk factor level. In contrast, methods based on marginal structural models (MSMs) can provide consistent estimates of causal effects when unmeasured confounding and model misspecification are absent. MSMs are a new class of causal models whose parameters are estimated using a new class of estimators-inverse-probability-of-treatment weighted estimators. We use a marginal structural Cox proportional hazards model to estimate the joint effect of zidovudine (AZT) and prophylaxis therapy for Pneumocystis carinii pneumonia on the survival of HIV-positive men in the Multicenter AIDS Cohort Study, an observational study of homosexual men. We obtained an estimated causal mortality rate (hazard) ratio of .67 (conservative 95% confidence interval .46-.98) for AZT and of 1.14 (.79, 1.64) for prophylaxis therapy. These estimates will be consistent for the true causal rate ratios when the functional forms chosen for our models are correct and data have been obtained on all time-independent and time-dependent covariates that predict both subsequent treatment and mortality.