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当重要混杂因素因死亡而缺失时,在观察性研究中估计幸存者平均因果效应。

On estimation of the survivor average causal effect in observational studies when important confounders are missing due to death.

作者信息

Egleston Brian L, Scharfstein Daniel O, MacKenzie Ellen

机构信息

Biostatistics Facility, Fox Chase Cancer Center, Philadelphia, Pennsylvania 19111-2497, USA.

出版信息

Biometrics. 2009 Jun;65(2):497-504. doi: 10.1111/j.1541-0420.2008.01111.x.

DOI:10.1111/j.1541-0420.2008.01111.x
PMID:18759833
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2700847/
Abstract

We focus on estimation of the causal effect of treatment on the functional status of individuals at a fixed point in time t* after they have experienced a catastrophic event, from observational data with the following features: (i) treatment is imposed shortly after the event and is nonrandomized, (ii) individuals who survive to t* are scheduled to be interviewed, (iii) there is interview nonresponse, (iv) individuals who die prior to t* are missing information on preevent confounders, and (v) medical records are abstracted on all individuals to obtain information on postevent, pretreatment confounding factors. To address the issue of survivor bias, we seek to estimate the survivor average causal effect (SACE), the effect of treatment on functional status among the cohort of individuals who would survive to t* regardless of whether or not assigned to treatment. To estimate this effect from observational data, we need to impose untestable assumptions, which depend on the collection of all confounding factors. Because preevent information is missing on those who die prior to t*, it is unlikely that these data are missing at random. We introduce a sensitivity analysis methodology to evaluate the robustness of SACE inferences to deviations from the missing at random assumption. We apply our methodology to the evaluation of the effect of trauma center care on vitality outcomes using data from the National Study on Costs and Outcomes of Trauma Care.

摘要

我们关注的是,在个体经历灾难性事件后的固定时间点t*,根据具有以下特征的观察性数据,估计治疗对其功能状态的因果效应:(i)事件发生后不久即实施治疗且治疗是非随机的;(ii)存活至t的个体被安排接受访谈;(iii)存在访谈无应答情况;(iv)在t之前死亡的个体缺失事件前混杂因素的信息;(v)对所有个体的医疗记录进行摘要以获取事件后、治疗前混杂因素的信息。为解决幸存者偏差问题,我们试图估计幸存者平均因果效应(SACE),即治疗对无论是否接受治疗都能存活至t的个体队列中功能状态的影响。为从观察性数据中估计这一效应,我们需要施加不可检验的假设,这些假设依赖于所有混杂因素的收集。由于在t之前死亡的个体缺失事件前信息,这些数据不太可能是随机缺失的。我们引入一种敏感性分析方法,以评估SACE推断对于偏离随机缺失假设的稳健性。我们将我们的方法应用于使用国家创伤护理成本与结果研究的数据来评估创伤中心护理对活力结果的影响。

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