Subgroup comparisons within and across studies in meta-analysis
元分析中研究内与跨研究的亚组比较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：摘要研究随机试验的亚组治疗效应及其异质性在跨研究汇总中的估计问题。
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…
元分析中研究内与跨研究的亚组比较
- 研究问题
- 随机试验之间的亚组构成不均衡，如何导致亚组治疗效应与交互效应的汇总不一致，并混淆试验内和跨试验证据？
- 方法与思路
- 作者在随机效应元分析中研究不同估计量，提出跨分析使用相同权重的 SWADA 方法，并结合解析、模拟和治疗试验实例。
- 材料陈述的贡献
- 摘要称统一权重可解决构成不均衡引起的不一致，降低偏差并改善覆盖率，同时报告推荐方案的可折叠性与交互效应 BLUE 性质。
- 阅读关注点
- 应核对试验内与跨试验信息的分解、统一权重的定义，以及覆盖率、可折叠性和 BLUE 结论各自需要的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出完整权重公式与适用条件，作者推荐不能视为无条件保证。
原文摘要
Subgroup-specific meta-analysis synthesizes treatment effects for patient subgroups across randomized trials. Methods include joint or separate modeling of subgroup effects and treatment-by-subgroup interactions, but inconsistencies arise when subgroup prevalence differs between studies (e.g., proportion of non-smokers). A key distinction is between study-generated evidence within trials and synthesis-generated evidence obtained by contrasting results across trials. This distinction matters for identifying which subgroups benefit or are harmed most. Failing to separate these evidence types can bias estimates and obscure true subgroup-specific effects, leading to misleading conclusions about relative efficacy. Standard approaches often suffer from such inconsistencies, motivating alternatives. We investigate standard and novel estimators of subgroup and interaction effects in random-effects meta-analysis and study their properties. We show that using the same weights across different analyses (SWADA) resolves inconsistencies from unbalanced subgroup distributions and improves subgroup and interaction estimates. Analytical and simulation studies demonstrate that SWADA reduces bias and improves coverage, especially under pronounced imbalance. To illustrate, we revisit recent meta-analyses of randomized trials of COVID-19 therapies. Beyond COVID-19, the findings outline a general strategy for correcting compositional bias in evidence synthesis, with implications for decision-making and statistical modeling. We recommend the Interaction RE-weights SWADA as a practical default when aggregation bias is plausible: it ensures collapsibility, maintains nominal coverage with modest width penalty, and yields BLUE properties for the interaction.