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.

翻译：在连续结局研究中，平均因果效应通常通过期望潜在结果的对比来定义。然而，当结局数据存在偏态分布时，期望值可能不再具有实际意义。实践中常见的做法是"忽略或转换"——要么完全忽略偏态性，要么对结局变量进行变换以获取更对称的分布，但这两种方法都无法令人满意。另一种替代方案是将因果效应重新定义为中位潜在结果的对比，但针对该参数的混杂调整方法讨论有限。本研究描述并比较了多种混杂调整方法以填补这一空白。所考察的方法包括多元分位数回归、逆概率加权（IPW）估计量、加权分位数回归以及两种鲜为人知的用于该问题的g计算实现。受澳大利亚儿童纵向研究中队列调查的启发，我们开展的模拟研究发现：当相关模型正确设定时，IPW估计量、加权分位数回归及g计算实现均能最大限度减少偏倚，其中g计算还能额外最小化方差。这些方法为常见的"忽略或转换"策略及多元分位数回归提供了具有吸引力的替代方案，增强了我们在偏态结局数据中获得有意义因果效应估计的能力。