An Approach to Nonparametric Inference on the Causal Dose Response Function
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因果剂量响应函数的非参数推断方法
- 研究问题
- 对连续暴露的因果剂量响应函数，如何在减少分布参数限制的同时检验其是否为常数，并构造同时置信带？
- 方法与思路
- 作者将常数函数原假设联系到剂量响应方差为零，构造方差估计量及其零假设极限分布，并通过检验反演构建置信带。
- 材料陈述的贡献
- 摘要称提出了可校准的非参数检验和同时置信带方法，并用模拟及 HIV 患者就医距离与持续就诊的案例进行评估。
- 阅读关注点
- 应核对因果识别条件、剂量响应方差所用的积分分布、零假设极限理论，以及检验反演形成同时置信带的步骤。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未说明所谓温和假设、平滑条件、置信带覆盖率或实证效应。
原文摘要
The causal dose response curve is commonly selected as the statistical parameter of interest in studies where the goal is to understand the effect of a continuous exposure on an outcome.Most of the available methodology for statistical inference on the dose-response function in the continuous exposure setting requires strong parametric assumptions on the probability distribution. Such parametric assumptions are typically untenable in practice and lead to invalid inference. It is often preferable to instead use nonparametric methods for inference, which only make mild assumptions about the data-generating mechanism. We propose a nonparametric test of the null hypothesis that the dose-response function is equal to a constant function. We argue that when the null hypothesis holds, the dose-response function has zero variance. Thus, one can test the null hypothesis by assessing whether there is sufficient evidence to claim that the variance is positive. We construct a novel estimator for the variance of the dose-response function, for which we can fully characterize the null limiting distribution and thus perform well-calibrated tests of the null hypothesis. We also present an approach for constructing simultaneous confidence bands for the dose-response function by inverting our proposed hypothesis test. We assess the validity of our proposal in a simulation study. In a data example, we study, in a population of patients who have initiated treatment for HIV, how the distance required to travel to an HIV clinic affects retention in care.