发表机构
Dartmouth College; University of Waterloo; Harvard T.H. Chan School of Public Health; Brown University School of Public Health（达特茅斯学院; 滑铁卢大学; 哈佛大学陈曾熙公共卫生学院; 布朗大学公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出在相对尺度可交换性假设下（风险比迁移性）估计失效时间结局的方法，应用于NLST至NHIS的筛查效果评估，发现风险比迁移性比分布迁移性更合理且估计准确。
AI 中文摘要
在某些临床领域，相对效应度量被认为在不同人群中比绝对度量保持更恒定。这表明，在相对尺度上的可交换性假设可能比在绝对尺度上的假设更合理。尽管如此，大多数将试验结果扩展到目标人群的方法依赖于试验人群与目标人群之间强且往往不切实际的分布可交换性假设（分布迁移性）。我们提出了在相对尺度上更合理的可交换性假设下失效时间结局的识别结果，主要关注风险比（风险比迁移性）。我们应用这些方法，利用国家肺部筛查试验（NLST）的数据，估计筛查策略对全因死亡率的影响，并将其扩展到来自国家健康访谈调查（NHIS）的具有全国代表性的目标人群。NHIS提供了全因死亡率数据，且筛查无混杂，从而可以对分布迁移性分析进行针对观察到的基线死亡风险的证伪评估。我们发现，这种方法显著低估了死亡风险。相比之下，风险比迁移性方法产生了与基线死亡风险相匹配的合理估计。因此，在分布迁移性失效的某些情况下，相对尺度上的假设可能更合理。
英文摘要
In certain clinical areas, relative effect measures are believed to remain more constant across populations than absolute measures. This suggests that exchangeability assumptions on the relative scale may be more plausible than those on the absolute scale. Despite this, most methods for extending trial results to a target population rely on strong and often implausible distributional exchangeability assumptions between the trial and target population (distributional transportability). We propose identification results for failure-time outcomes under a more plausible assumption of exchangeability on the relative scale, focusing primarily on the risk ratio (risk ratio transportability). We applied these methods to estimate the effect of screening strategies on all-cause mortality, using data from the National Lung Screening Trial (NLST) to a nationally representative target population from the National Health Interview Survey (NHIS). All-cause mortality data was available from NHIS, along with no confounding of screening, allowing a falsification assessment of the distributional transportability analysis against the observed baseline risk.We found that this approach substantially underestimated mortality risk. In comparison, the risk ratio transportability approach produced reasonable estimates that matched the baseline risk.Thus, assumptions on relative scales may be more plausible in certain settings where distributional transportability fails.
