发表机构
Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DR-Meta方法，将设计评分递减异质性作为可证伪假设，在位置-尺度框架下约束检验，并证明其在错误方向或U形异质性下可优雅回退至随机效应模型，主要贡献为诊断性而非自动效率提升。
AI 中文摘要
分析者不应仅因为设计评分被标记为稳健性，就假设它能改善Meta分析。本文将沿预设的、按结果分离的设计评分递减的异质性视为一种可证伪的方向性假设。在一个包含18项研究的示例中，约束模型落在γ=0处，并在数值上与常规随机效应模型完全一致，而无约束的尺度拟合则指向相反方向。故意违反约束的模拟显示，在错误方向和U形异质性下，模型具有相同的优雅回退；步进递减方差函数即使在函数形式误设下仍保持效率增益。DR-Meta将假设形式化为τ²(DR)=τ₀²exp(-γDR)，γ≥0，置于既定的位置-尺度框架内。推导并给出了完全证明的精确嵌套性、条件权重单调性、固定效应上界、均值误设下的伪真目标以及评分重新缩放行为。在经验锚定的γ接近1时，效率增益可忽略不计，仅在更强的梯度下才变得显著；最大的RMSE降低约9.6%，发生在刻意极端的条件下。插件区间相对于随机效应覆盖不足，修正的Knapp-Hartung调整仅适度改善覆盖。扩大γ优化边界对γ估计的影响远大于对合并估计RMSE的影响。因此，实际贡献是诊断性的而非自动效率提升：只要同时报告约束、无约束、位置和敏感性分析，嵌套的方向性限制可以得到支持、反驳，或透明地退化为随机效应模型。
英文摘要
An analyst should not assume that a design score improves a meta-analysis simply because it is labeled as robustness. This paper treats declining heterogeneity along a prespecified, outcome-separated design score as a falsifiable directional hypothesis. In an 18-study illustration, the constrained model lands at $γ= 0$ and becomes numerically identical to conventional random effects, while the unrestricted scale fit points in the opposite direction. Simulations that deliberately violate the restriction show the same graceful fallback under wrong-direction and U-shaped heterogeneity; a stepwise decreasing variance function retains an efficiency gain despite functional-form misspecification. DR-Meta formalizes the hypothesis as $τ^2(\mathrm{DR}) = τ_0^2 \exp(-γ\mathrm{DR})$, $γ\geq 0$, within an established location-scale framework. Exact nesting, conditional weight monotonicity, a fixed-effect upper bound, pseudo-true targets under mean misspecification, and score-rescaling behavior are derived with full proofs. Efficiency gains are negligible around an empirically anchored $γ$ near 1 and become material only under stronger gradients; the largest RMSE reduction, about 9.6 percent, occurs in a deliberately extreme condition. Plug-in intervals under-cover relative to random effects, and modified Knapp-Hartung adjustment improves coverage only modestly. Widening the $γ$ optimization bound changes $γ$ estimates much more than pooled-estimate RMSE. The practical contribution is therefore diagnostic rather than automatic efficiency: a nested directional restriction can be supported, contradicted, or collapse transparently to random effects, provided constrained, unrestricted, location, and sensitivity analyses are reported together.
Comments42 pages, 8 figures. R package drmeta v0.2.2. Reproducibility materials are included as ancillary files
