Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv
利用 Stata standsurv 回归标准化估计竞争事件下的因果效应AI 解读 ↓
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利用 Stata standsurv 回归标准化估计竞争事件下的因果效应
- 研究问题
- 当竞争事件阻止目标事件发生时，总效应、直接效应与可分离效应分别回答什么问题，如何通过生存模型和标准化估计它们？
- 方法与思路
- 作者拟合生存模型后，将个体层面的预测在研究人群中取平均，利用 standsurv 计算差值、比值等对比，并用 delta 方法构造区间。
- 材料陈述的贡献
- 摘要称，文章解释竞争事件下不同因果效应的含义及其标准化实现，并提供公开前列腺癌数据示例，供读者复现和探索。
- 阅读关注点
- 应核对各效应所对应的干预定义与识别假设、竞争事件的处理、生存模型设定，以及标准化人群和置信区间计算方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出各效应的识别条件，命令能计算对比并不自动赋予其因果解释。
原文摘要
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 statistical 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 interpretation. 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 effect can be partitioned into its effect on the event of interest and its effect on 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.