论文标题
中位数因果关系差异的混杂调整方法
Confounding-adjustment methods for the causal difference in medians
论文作者
论文摘要
通过连续的结果，通常使用预期潜在结果的对比度来定义平均因果效应。但是，在存在偏斜的结果数据的情况下，期望可能不再有意义。在实践中，典型的方法是“忽略或转化” - 完全忽略偏度或转化结果以获得更对称分布，尽管两种方法都不令人满意。或者，可以将因果效应重新定义为中位潜在结果的对比，但讨论了估计该参数的混杂调整方法的讨论受到限制。在这项研究中，我们描述并比较了解决这一差距的混杂调整方法。所考虑的方法是多变量的分位数回归，一个反比概率加权（IPW）估计器，加权回归和两个鲜为人知的G-Compontaunt的实现。在澳大利亚儿童纵向研究中的一项队列研究中，我们进行了一项模拟研究，发现当正确指定相关模型时，发现IPW估计器，加权分位数回归和G型实施最小化的偏差，并额外降低了差异。这些方法为常见的“忽略或转化”方法和多变量分数回归提供了有吸引力的替代方法，从而增强了我们通过偏斜的结果数据获得有意义的因果效应估计的能力。
With continuous outcomes, the average causal effect is typically defined using a contrast of expected potential outcomes. However, in the presence of skewed outcome data, the expectation may no longer be meaningful. In practice the typical approach is to either "ignore or transform" - ignore the skewness altogether or transform the outcome to obtain a more symmetric distribution, although neither approach is entirely satisfactory. Alternatively the causal effect can be redefined as a contrast of median potential outcomes, yet discussion of confounding-adjustment methods to estimate this parameter is limited. In this study we described and compared confounding-adjustment methods to address this gap. The methods considered were multivariable quantile regression, an inverse probability weighted (IPW) estimator, weighted quantile regression and two little-known implementations of g-computation for this problem. Motivated by a cohort investigation in the Longitudinal Study of Australian Children, we conducted a simulation study that found the IPW estimator, weighted quantile regression and g-computation implementations minimised bias when the relevant models were correctly specified, with g-computation additionally minimising the variance. These methods provide appealing alternatives to the common "ignore or transform" approach and multivariable quantile regression, enhancing our capability to obtain meaningful causal effect estimates with skewed outcome data.
