AI 中文总结
研究指出元分析中发表偏倚测试功效低，应转向用置信区间估计偏倚，逐步函数选择模型置信区间过窄，z曲线的置信区间有标称覆盖率，是检测发表偏倚的 valuable 工具，还通过实例说明偏倚估计意义。
AI 中文摘要
元分析者经常测试发表偏倚，但非显著测试结果不具决定性，因其可能是二类错误。在大型析因模拟中，研究表明一旦效应大小异质且数据不符合模型假设，即便有1000项研究，发表偏倚测试功效仍低。应从检测偏倚转向用置信区间估计偏倚。逐步函数选择模型的置信区间过窄，而z曲线通过将预期发现率转换为选择权重参数获得的置信区间具有标称覆盖率，是检测发表偏倚的有价值工具，文中还通过社会启动元分析及一组应用心理学元分析举例说明了偏倚估计的意义。
英文摘要
Meta-analysts routinely test for publication bias, but a nonsignificant test is inconclusive because it may be a Type 2 error. In a large factorial simulation, I show that publication-bias tests have low power even with 1,000 studies once effect sizes are heterogeneous and the data do not meet the model's assumptions. I argue that the goal should shift from detecting bias to estimating it with confidence intervals. A confidence interval does not only bound the hypothesis that bias is absent; its upper bound also indicates how much bias remains compatible with the data. When the interval is wide, the upper bound does not exclude a large amount of bias, even if the formal test is nonsignificant. I then show that confidence intervals from step-function selection models are too narrow, covering the true amount of bias only about half the time. In contrast, z-curve's confidence interval obtained by transforming the expected discovery rate into a selection weight parameter has nominal coverage. Z-curve is therefore a valuable tool for examining publication bias in meta-analyses, especially when heterogeneity is high. I illustrate the implications of bias estimation with a meta-analysis of social priming and a set of applied-psychology meta-analyses.
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