基于样本分割的观察性研究中贫困对心血管疾病风险因素影响的多变量敏感性分析
Powerful Multivariate Sensitivity Analysis via Sample Splitting in an Observational Study of the Effects of Poverty on Cardiovascular Disease Risk Factors
AI总结：
提出通过样本分割（规划样本选择最优线性组合，分析样本进行推断）来增强观察性研究中多结局全局零假设检验对未测量偏倚的敏感性分析功效，并应用于贫困对儿童青少年心血管疾病风险因素影响的研究。
摘要：
在评估观察性研究中暴露对两个或更多结局的因果效应时，结局的线性组合可能降低全局零假设检验对潜在未测量偏倚的敏感性。虽然可以使用Scheffe投影或其约束变体考虑所有评分结局的线性组合，但找到最小化对未测量偏倚敏感性的组合需要对多重检验进行校正，这会削弱功效，尤其是当许多结局感兴趣时。为了缓解这一问题，我们提出将样本分割为规划样本（用于识别最优线性组合）和分析样本（用于进行推断）。我们提供了该方法的线性组合集的新颖刻画，保证该方法与全样本替代方法实现相同的渐近功效，并进行了广泛的模拟研究，证明在有限样本中增强了功效。最后，我们将该方法应用于研究贫困对儿童和青少年心血管疾病风险因素出现的影响。我们发现了与身体成分、体力活动和烟草暴露相关的结局的不良后果。尽管贫困对高烟草暴露的影响对未测量混杂因素表现出一定的稳健性，但其他发现仍然对潜在偏倚敏感。
英文摘要：
When assessing the causal effect of an exposure on two or more outcomes in an observational study, a linear combination of outcomes may lessen the sensitivity of a test of the global null hypothesis to potential unmeasured biases. While all linear combinations of scored outcomes can be considered using Scheffe projections or constrained variants thereof, finding the combination that minimizes sensitivity to unmeasured biases requires corrections for multiple testing, which can erode power, especially when many outcomes are of interest. To mitigate this issue, we propose splitting the sample into a planning sample to identify an optimal linear combination and an analysis sample to conduct inference. We provide a novel characterization of the set of linear combinations for which this approach is guaranteed to achieve the same asymptotic power as full-sample alternatives and conduct extensive simulation studies that demonstrate enhanced power in finite samples. Finally, we apply our method to investigate the effects of poverty on the emergence of cardiovascular disease risk factors in children and adolescents. We discover adverse consequences on outcomes related to body composition, physical activity, and tobacco exposure. Although the impact of poverty on elevated tobacco exposure shows some robustness to unmeasured confounding, the other findings remain sensitive to potential biases.
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