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基于回归的、存在未测量效应修饰因子时的冲突试验近端协调

Regression-Based Proximal Reconciliation of Conflicting Trials with Unmeasured Effect Modifiers

Daniel A Xu, Eric J Tchetgen Tchetgen, Enrique F Schisterman, Sean C Blackwell, Ellen C Caniglia

arXiv 2608.04202首次发表:更新:

AI 中文总结

本研究针对存在未测量效应修饰因子的冲突试验,构建因果推断框架,开发基于回归的检验方法与等价检验框架,引入协调比例,通过实例分析为解释试验结果冲突提供依据。

AI 中文摘要

当相关效应修饰因子的分布在不同研究人群间存在差异时,具有相似方案的随机对照试验可能会产生相互矛盾的结果。尽管该问题对证据合成和监管决策至关重要,但目前尚无正式的统计框架用于定义和评估冲突试验是否可协调。为填补这一空白,我们开发了一种因果推断框架,用于在存在未测量效应修饰因子的情况下,评估加性和乘性尺度上的条件与边际可协调性。在该框架内,我们使用假设的未测量效应修饰因子的代理变量,开发了基于回归的、参数结构模型下的条件可协调性检验方法。为评估边际可协调性,我们扩展了现有的可迁移性方法,构建了一个等价检验框架;同时引入了协调比例,用于量化边际协调的程度。我们利用关于17α-羟孕酮己酸酯预防复发性早产的Meis试验与PROLONG试验的冲突结果来例证这些方法。分析提供的有限证据表明,由所选代理变量捕捉的未测量效应修饰因子(如宫颈长度),足以在边际层面协调这两项试验。这些发现表明,近端协调方法可帮助监管机构、研究人员和临床医生评估研究人群的差异是否能解释试验结果的冲突。

英文摘要

Randomized controlled trials with similar protocols may yield conflicting findings when the distribution of relevant effect modifiers differs across study populations. Yet no formal statistical framework exists for defining and assessing whether conflicting trials are reconcilable, despite the importance of this question for evidence synthesis and regulatory decision making. To address this gap, we develop a causal inference framework for evaluating conditional and marginal reconcilability on additive and multiplicative scales in the presence of unmeasured effect modifiers. Within this framework, we use proxy variables for hypothesized unmeasured effect modifiers to develop regression-based tests of conditional reconcilability under parametric structural models. To assess marginal reconcilability, we extend existing transportability methods and develop an equivalence testing framework. We also introduce a reconciliation proportion to quantify the degree of marginal reconciliation. We illustrate these methods using the conflicting Meis and PROLONG trials of 17-alpha-hydroxyprogesterone caproate for preventing recurrent preterm birth. The analyses provided limited evidence that unmeasured effect modifiers such as cervical length, as captured by the selected proxies, were sufficient to marginally reconcile the trials. These findings demonstrate how proximal reconciliation methods may help regulators, researchers, and clinicians evaluate whether differences in study populations explain conflicting trial findings.

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