Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
BMC Med Res Methodol. 2022 Aug 13;22(1):226. doi: 10.1186/s12874-022-01666-x.
When interested in a time-to-event outcome, competing events that prevent the occurrence of the event of interest may be present. In the presence of competing events, various estimands have been suggested for defining the causal effect of treatment on the event of interest. Depending on the estimand, the competing events are either accommodated or eliminated, resulting in causal effects with different interpretations. The former approach captures the total effect of treatment on the event of interest while the latter approach captures the direct effect of treatment on the event of interest that is not mediated by the competing event. Separable effects have also been defined for settings where the treatment can be partitioned into two components that affect the event of interest and the competing event through different causal pathways.
We outline various causal effects that may be of interest in the presence of competing events, including total, direct and separable effects, and describe how to obtain estimates using regression standardisation with the Stata command standsurv. Regression standardisation is applied by obtaining the average of individual estimates across all individuals in a study population after fitting a survival model.
With standsurv several contrasts of interest can be calculated including differences, ratios and other user-defined functions. Confidence intervals can also be obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer to allow the reader to replicate the analysis and further explore the different effects of interest.
Several causal effects can be defined in the presence of competing events and, under assumptions, estimates of those can be obtained using regression standardisation with the Stata command standsurv. The choice of which causal effect to define should be given careful consideration based on the research question and the audience to which the findings will be communicated.
当关注一个时间事件结果时，可能会存在阻止感兴趣事件发生的竞争事件。在存在竞争事件的情况下，已经提出了各种估计量来定义治疗对感兴趣事件的因果效应。根据估计量的不同，竞争事件要么被包含，要么被排除，从而产生具有不同解释的因果效应。前者方法捕捉了治疗对感兴趣事件的总效应，而后者方法捕捉了治疗对感兴趣事件的直接效应，该效应不受竞争事件的影响。在治疗可以分为两个部分的情况下，也定义了可分离效应，这两个部分通过不同的因果途径影响感兴趣事件和竞争事件。
我们概述了在存在竞争事件时可能感兴趣的各种因果效应，包括总效应、直接效应和可分离效应，并描述了如何使用 Stata 命令 standsurv 进行回归标准化来获得估计值。回归标准化是通过在拟合生存模型后在研究人群中的所有个体中获得个体估计值的平均值来应用的。
使用 standsurv，可以计算出几个感兴趣的对比，包括差异、比率和其他用户定义的函数。也可以使用差值法获得置信区间。在整个过程中，我们使用一个公共的前列腺癌数据集进行分析，以允许读者复制分析并进一步探索感兴趣的不同效应。
在存在竞争事件的情况下，可以定义几个因果效应，并在假设下，使用 Stata 命令 standsurv 进行回归标准化来获得这些效应的估计值。应该根据研究问题和将向其传达研究结果的受众仔细考虑选择定义哪个因果效应。
