We propose a novel, intuitive measure of statistical performance: precision-weighted bias. Precision-weighted bias is defined as the unconditional bias of an estimator weighted by the degree of information (precision) it contains. Current guidelines, such as GRADE and CONSORT, often view the potential for increased bias in adaptive designs as a deterrent for the inclusion of such designs in systematic reviews. However, we demonstrate that the bias in a common-effect meta-analysis is approximately equal to the precision-weighted average of the precision-weighted biases of its constituent studies, rather than of their unweighted unconditional biases. Through simulation studies, we show that while adaptive designs may exhibit unweighted bias, they frequently have zero precision-weighted bias. Consequently, including these designs often results in a negligible change to the overall meta-analysis bias. These results suggest that precision-weighted bias is a superior indicator for determining whether to include an adaptive design in a meta-analysis. We recommend that precision-weighted bias be used as a standard complement to unweighted unconditional and conditional bias in simulation studies to support more inclusive and accurate evidence synthesis.

翻译：摘要：我们提出了一种新颖且直观的统计性能指标：精度加权偏倚。精度加权偏倚定义为估计量的无条件偏倚按其包含的信息量（精度）加权后的结果。当前指南（如GRADE和CONSORT）常将适应性设计中潜在的偏倚增加视为在系统综述中纳入此类设计的阻碍。然而，我们证明在共同效应元分析中，总体偏倚近似等于各组成研究的精度加权偏倚的精度加权平均值，而非其未加权的无条件偏倚的平均值。通过模拟研究，我们发现尽管适应性设计可能表现出未加权的偏倚，但其精度加权偏倚通常为零。因此，纳入这些设计往往对整体元分析偏倚产生可忽略的影响。这些结果表明，精度加权偏倚是判断是否在元分析中纳入适应性设计的更优指标。我们建议在模拟研究中将精度加权偏倚作为未加权无条件偏倚和条件偏倚的标准补充指标，以支持更具包容性和准确性的证据综合。