Estimating causal effects of time- dependent exposures on a binary endpoint in a high-dimensional setting
Estimating causal effects of time- dependent exposures on a binary endpoint in a high-dimensional setting
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DOI：
10.1186/s12874-018-0527-5
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发表时间：
2018-07-03
影响因子：
4
通讯作者：
Lanoy, Emilie
中科院分区：
文献类型：
--
作者：
Asvatourian, Vahe;Coutzac, Clelia;Lanoy, Emilie
背景资料：最近，当DAG不存在时的干预演算（IDA）方法被开发用于从观察的高维数据估计因果效应的下限。最初引入它是为了评估不随时间变化的基线生物标志物的影响。然而，在许多临床环境中，生物标志物的测量在治疗期间的固定时间点重复，因此，该方法需要扩展。本文的目的是扩展IDA的第一步，彼得克拉克（PC）算法，一个时间依赖性的曝光的背景下，一个二进制outcome.Methods：我们概括了所谓的“PC算法”，以考虑到时间顺序的重复测量的曝光，并建议应用IDA与我们的新版本，按时间顺序排列的PC算法（COPC算法）。扩展包括Firth的校正。在应用时间依赖性免疫生物标志物对转移性黑色素瘤患者毒性、死亡和进展的因果效应估计方法之前，进行了模拟研究。结果：模拟研究表明，使用COPC算法获得的完全部分有向无环图（CPDAG）在结构上比使用PC算法获得的CPDAG更接近真实的CPDAG。此外，因果效应更准确时，他们估计的基础上获得的CPDAG使用COPC算法。此外，通过COPC算法获得的CPDAG允许去除非时间顺序箭头，其中在时间t测量的变量指向在时间t'测量的变量，其中t' < t。双向边缘在使用COPC算法获得的CPDAG中较少，这支持了从这些CPDAG估计的因果效应的变异性较小的事实。在这个例子中，0.5%的阈值的比较误差率导致选择一个可解释的一组biologicals.Conclusions：COPC算法提供CPDAG，保持目前的数据中的时序结构，从而允许估计的时间依赖性免疫生物标志物对早期毒性，过早死亡和进展的因果关系的下限。
Background: Recently, the intervention calculus when the DAG is absent (IDA) method was developed to estimate lower bounds of causal effects from observational high-dimensional data. Originally it was introduced to assess the effect of baseline biomarkers which do not vary over time. However, in many clinical settings, measurements of biomarkers are repeated at fixed time points during treatment and, therefore, this method needs to be extended. The purpose of this paper is to extend the first step of the IDA, the Peter Clarks (PC)-algorithm, to a time-dependent exposure in the context of a binary outcome.Methods: We generalised the so-called "PC-algorithm" to take into account the chronological order of repeated measurements of the exposure and proposed to apply the IDA with our new version, the chronologically ordered PC-algorithm (COPC-algorithm). The extension includes Firth's correction. A simulation study has been performed before applying the method for estimating causal effects of time-dependent immunological biomarkers on toxicity, death and progression in patients with metastatic melanoma.Results: The simulation study showed that the completed partially directed acyclic graphs (CPDAGs) obtained using COPC-algorithm were structurally closer to the true CPDAG than CPDAGs obtained using PC-algorithm. Also, causal effects were more accurate when they were estimated based on CPDAGs obtained using COPC-algorithm. Moreover, CPDAGs obtained by COPC-algorithm allowed removing non-chronological arrows with a variable measured at a time t pointing to a variable measured at a time t' where t' < t. Bidirected edges were less present in CPDAGs obtained with the COPC-algorithm, supporting the fact that there was less variability in causal effects estimated from these CPDAGs. In the example, a threshold of the per-comparison error rate of 0.5% led to the selection of an interpretable set of biomarkers.Conclusions: The COPC-algorithm provided CPDAGs that keep the chronological structure present in the data and thus allowed to estimate lower bounds of the causal effect of time-dependent immunological biomarkers on early toxicity, premature death and progression.