Sensitivity Analysis for Matched Observational Studies with Continuous Exposures and Binary Outcomes
连续暴露与二元结局匹配观察研究的敏感性分析AI 解读 ↓
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连续暴露与二元结局匹配观察研究的敏感性分析
- 研究问题
- 一般匹配设计下，如何评估连续暴露与二元结局的因果结论对未测混杂的敏感性，并允许处理效应异质性？
- 方法与思路
- 作者用概率格理论建立尖锐原假设下的精确分析，推导设计敏感性，并以混合整数规划处理广义归因效应。
- 材料陈述的贡献
- 摘要称，建立有限总体精确性及渐近推断结果，并指出连续暴露与连续结局的一般敏感性分析具有 NP 难计算性质。
- 阅读关注点
- 应核对敏感性参数、匹配设计范围、尖锐原假设与归因效应的区别，以及精确性条件、复杂性结论和求解规模。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明具体条件，一般计算难度也不能直接等同于实际无法求解。
原文摘要
Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion's sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well-established for various matching designs and are commonly used in various studies. However, unlike the binary exposure case, there still lacks valid and general sensitivity analysis methods for continuous exposures, except in some special cases such as pair matching. To fill this gap in the binary outcome case, we develop a sensitivity analysis framework for general matching designs with continuous exposures and binary outcomes. First, we use probabilistic lattice theory to show our sensitivity analysis approach is finite-population-exact under Fisher's sharp null. Second, we prove a novel design sensitivity formula as a powerful tool for asymptotically evaluating the performance of our sensitivity analysis approach. Third, to allow effect heterogeneity with binary outcomes, we introduce a framework for conducting asymptotically exact inference and sensitivity analysis on generalized attributable effects with binary outcomes via mixed-integer programming. Fourth, for the continuous outcomes case, we show that conducting an asymptotically exact sensitivity analysis in matched observational studies when both the exposures and outcomes are continuous is generally NP-hard, except in some special cases such as pair matching. As a real data application, we apply our new methods to study the effect of early-life lead exposure on juvenile delinquency. We also develop a publicly available R package for implementation of the methods in this work.