Anchors Away: Navigating Unanchored Indirect Comparisons with Multilevel Unanchored Meta-Regression (ML-UMR)
多层无锚点元回归：无共同对照的间接治疗比较AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确讨论治疗效应识别、条件可交换性和向目标人群迁移，属于因果推断；证据网络不等同于个体网络干扰。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
多层无锚点元回归：无共同对照的间接治疗比较
- 研究问题
- 当单臂研究或完全不连通证据缺乏共同对照时，如何整合个体与汇总数据，比较多种治疗并区分效应识别与目标人群迁移？
- 方法与思路
- 作者提出贝叶斯多层无锚点元回归，在统一似然中联合建模个体和汇总数据，估计不同治疗、研究及人群的边际与条件效应。
- 材料陈述的贡献
- 摘要称，方法使强识别假设显式化；模拟中比较人群效应偏差低、覆盖率达标，但迁移依赖共享预后因素等假设及亚组信息。
- 阅读关注点
- 核对条件可交换性、结局模型和共享预后因素假设，区分识别与迁移要求，并检查强效应修饰及亚组信息对结论的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要明确指出无锚点比较依赖强且常不可验证的假设，框架并未减弱要求。
原文摘要
Unanchored indirect treatment comparisons using single-arm studies or disconnected evidence are increasingly used in health technology assessment (HTA) when randomized evidence is unavailable. Existing methods, including matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC), are generally limited to pairwise settings and typically estimate marginal effects in the comparator study population, which may differ from the decision-relevant population. We propose multilevel unanchored meta-regression (ML-UMR), a Bayesian regression framework for synthesizing individual patient data and aggregate data from fully disconnected evidence. ML-UMR extends multilevel network meta-regression (ML-NMR) to unanchored settings by jointly modeling individual- and aggregate-level data within a unified likelihood, enabling estimation of treatment-specific outcomes and both marginal and conditional effects across multiple treatments, studies, and target populations. ML-UMR distinguishes assumptions required to identify treatment effects from those required to transport results to target populations. As with all unanchored comparisons, valid inference relies on strong and often unverifiable assumptions, including conditional exchangeability, correct specification of the outcome model, and cross-treatment assumptions (e.g., shared prognostic factor assumption (SPFA)). ML-UMR does not lessen these requirements but makes them explicit within a unified framework and facilitates sensitivity analyses. In simulation studies, ML-UMR produced low bias and nominal coverage for comparator-population effects. Transportability to alternative populations depended critically on identifying assumptions: violations of SPFA led to bias under strong effect modification, whereas incorporating subgroup information restored near-unbiased estimation and nominal coverage.