Integration of aggregated data in causally interpretable meta-analysis by inverse weighting
通过逆向加权将汇总数据纳入可作因果解释的元分析AI 解读 ↓
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通过逆向加权将汇总数据纳入可作因果解释的元分析
- 研究问题
- 当试验之间效应修饰变量的分布不同，且部分试验无法提供个体数据时，如何将汇总数据纳入面向目标人群的因果元分析？
- 方法与思路
- 作者扩展基于矩的群体调整方法，用汇总数据支持病例构成标准化，并开发计算方法处理常规 M 估计推断所需信息不足的问题。
- 材料陈述的贡献
- 摘要称，方法允许整合无法获取个体数据的试验，并通过模拟及银屑病药物比较应用评估有限样本表现和实际可用性。
- 阅读关注点
- 应核对效应向目标人群迁移的识别条件、汇总矩信息需求、标准化与汇总步骤，以及计算推断怎样处理缺失的汇总信息。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收状态未核实。摘要未列出迁移条件、信息需求或实证数值，不能判断药物比较的因果可信度。
原文摘要
Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between eligible trials. To overcome this, recent work on transportability methods has considered standardizing results of individual studies over the case-mix of a target population, prior to pooling them as in a classical random-effect meta-analysis. One practical challenge, however, is that case-mix standardization often requires individual participant data (IPD) on outcome, treatments and case-mix characteristics to be fully accessible in every eligible study, along with IPD case-mix characteristics for a random sample from the target population. In this paper, we aim to develop novel strategies to integrate aggregated-level data from eligible trials with non-accessible IPD into a causal meta-analysis, by extending moment-based methods frequently used for population-adjusted indirect comparison in health technology assessment. Since valid inference for these moment-based methods by M-estimation theory requires additional aggregated data that are often unavailable in practice, computational methods to address this concern are also developed. We assess the finite-sample performance of the proposed approaches by simulated data, and then apply these on real-world clinical data to investigate the effectiveness of risankizumab versus ustekinumab among patients with moderate to severe psoriasis.