Lesko Catherine R, Lau Bryan
From the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Epidemiology. 2017 Jan;28(1):20-27. doi: 10.1097/EDE.0000000000000565.
Epidemiologic studies that aim to estimate a causal effect of an exposure on a particular event of interest may be complicated by the existence of competing events that preclude the occurrence of the primary event. Recently, many articles have been published in the epidemiologic literature demonstrating the need for appropriate models to accommodate competing risks when they are present. However, there has been little attention to variable selection for confounder control in competing risk analyses.
We employ simulation to demonstrate the bias in two variable selection strategies include covariates that are associated with the exposure and (1) which change the cause-specific hazard of any of the outcomes; or (2) which change the cause-specific hazard of the specific event of interest.
We demonstrated minimal to no bias in estimators adjusted for confounders of exposure and either the event of interest or the competing event, but bias of varying magnitude in almost all estimators adjusted only for confounders of exposure and the primary outcome.
When estimating causal effects for which there are competing risks, the analysis should control for confounders of both the exposure-primary outcome effect and of the exposure-competing outcome effect.
旨在估计暴露对特定感兴趣事件的因果效应的流行病学研究，可能会因存在竞争事件而变得复杂，这些竞争事件会阻止主要事件的发生。最近，流行病学文献中发表了许多文章，表明在存在竞争风险时需要适当的模型来处理。然而，在竞争风险分析中，很少有人关注混杂因素控制的变量选择。
我们采用模拟来展示两种变量选择策略中的偏差，这两种策略包括与暴露相关的协变量，以及（1）会改变任何一种结局的特定病因风险；或（2）会改变特定感兴趣事件的特定病因风险。
我们证明，对于根据暴露和感兴趣事件或竞争事件的混杂因素进行调整的估计量，偏差极小或无偏差，但几乎所有仅根据暴露和主要结局的混杂因素进行调整的估计量都存在不同程度的偏差。
在估计存在竞争风险的因果效应时，分析应控制暴露-主要结局效应和暴露-竞争结局效应的混杂因素。
