Confounding-adjustment methods for the causal difference in medians
因果中位数差的混杂调整方法AI 解读 ↓
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因果中位数差的混杂调整方法
- 研究问题
- 面对偏斜连续结局，若以两种潜在结果的中位数差定义因果效应，哪些混杂调整方法能有效估计这一目标？
- 方法与思路
- 作者比较多变量分位数回归、逆概率加权、加权分位数回归及两种 g-computation 实现，并开展队列研究启发的模拟。
- 材料陈述的贡献
- 摘要称相关模型正确设定时，加权方法与两种 g-computation 实现可减小偏差，后者还减小方差，为偏斜结局提供替代方案。
- 阅读关注点
- 应核对潜在结果边际中位数差与条件分位数系数的区别、各方法所需模型，以及模拟中偏差和方差比较的设定。
- 阅读边界
- 来源为 arXiv 预印本，同行评审及期刊接收未经核实；摘要结论限于所述正确设定情形，未说明具体识别条件或误设表现。
原文摘要
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.