Greenland S
Department of Epidemiology, University of California, Los Angeles School of Public Health.
Am J Epidemiol. 1994 Aug 1;140(3):290-6. doi: 10.1093/oxfordjournals.aje.a117248.
Meta-analysis is essential for obtaining reproducible summaries of study results and valuable for discovering patterns among study results. A good meta-analysis will highlight and delineate the subjective components of these processes and vigorously search for sources of heterogeneity. Unfortunately, these objective are not always met by common techniques. For example, a scatterplot is an objective summarization if the data are uncensored, but inferred patterns should be regarded as subjective recognitions of the analyst, not objective data properties. Random-effects summaries encourage averaging over important data patterns, divert attention from key sources of heterogeneity, and can amplify distortions produced by publication bias; such summaries should only be used when important heterogeneity remains after a thorough search for the sources of such heterogeneity. Quality scoring adds the analyst's subjective bias to the results, wastes information, and can prevent the recognition of key sources of heterogeneity; it should be completely replaced by meta-regression on quality items (the score components).
荟萃分析对于获得可重复的研究结果总结至关重要，并且对于发现研究结果之间的模式很有价值。一项好的荟萃分析将突出并描述这些过程的主观成分，并积极寻找异质性来源。不幸的是，常见技术并不总是能实现这些目标。例如，如果数据未被删失，散点图是一种客观的总结，但推断出的模式应被视为分析师的主观认知，而非客观的数据属性。随机效应总结鼓励对重要数据模式进行平均，转移对异质性关键来源的注意力，并可能放大发表偏倚产生的偏差；只有在彻底寻找此类异质性来源后仍存在重要异质性时，才应使用此类总结。质量评分会将分析师的主观偏差添加到结果中，浪费信息，并可能妨碍对异质性关键来源的识别；它应该完全被基于质量项目（评分成分）的元回归所取代。
