AI 中文总结
本研究针对近期误判UWLS应用价值的研究，论证无约束加权最小二乘（UWLS）在医学meta分析中比传统随机效应模型（RE）拟合更优、统计特性更好，应常规报告。
AI 中文摘要
无约束加权最小二乘（UWLS）的均值效应meta分析估计量是传统随机效应模型（RE）的替代方案，它是一种加权最小二乘回归估计量，可表示为乘法随机效应模型。在Cochrane系统评价数据库（CDSR）的广泛研究中，通过AIC/BIC衡量，UWLS被证明比RE更适合医学研究。UWLS的均值和异质性估计量的独立性带来了RE不具备的小样本优势。RE的异质性方差估计存在较大的小样本偏差和不确定性，这与RE的边界问题（即RE的异质性方差被估计为零）共同解释了RE拟合效果相对较差的大部分原因。我们证明，UWLS在RE的边界处几乎总是具有更优的拟合效果，仅存在罕见例外。此外，在由四个不同研究团队发表的研究汇编的1665种模拟设计中，UWLS在偏差、均方误差（MSE）和覆盖率方面的统计特性总体上优于RE。近期本期刊的一项研究在CDSR及一组新模拟中广泛重复了UWLS更优拟合度的结果。但该研究因报告和解读错误，呼吁继续将RE作为默认meta分析估计量，仅有限应用UWLS。我们回应了该近期研究的担忧，并说明为何UWLS应在医学meta分析中常规报告。
英文摘要
The unrestricted weighted least squares (UWLS) meta-analysis estimator of mean effect is an alternative to the conventional random-effects model (RE). It is a weighted least squares regression estimator that can be represented as a multiplicative random-effects model. UWLS has been shown to fit medical research better than RE as measured by AIC/BIC widely across Cochrane Database of Systematic Reviews (CDSR). The independence of UWLS's mean and heterogeneity estimators provide small-sample advantages that RE does not possess. Large small-sample biases and uncertainty in RE's heterogeneity variance estimates explain most of RE's relatively poor fit along with RE's boundary problem, where RE's heterogeneity variance is estimated to be zero. We prove that UWLS almost always has superior fit at RE's boundary with uncommon exceptions. UWLS has also been found to have generally superior statistical properties: bias, MSE, and coverage relative to RE across 1,665 simulation designs compiled from four published studies authored by different teams of researchers. A recent study in this journal replicated UWLS's superior goodness of fit widely across both the CDSR and a new set of simulations. Due to reporting and interpretation errors, this recent study calls for the continued use of RE as the default meta-analysis estimator with limited applications of UWLS. We address this recent study's concerns and show why UWLS should be routinely reported in medical meta-analyses.
