虚假关系
计量经济学
质量(理念)
可靠性
统计假设检验
一致性(知识库)
统计
综合测试
出版偏见
荟萃分析
点估计
医学
置信区间
子群分析
心理学
数学
内科学
哲学
几何学
认识论
政治学
法学
作者
Gordon Guyatt,Andrew D Oxman,Regina Kunz,James Woodcock,Jan Brożek,Mark Helfand,Pablo Alonso‐Coello,Paul Glasziou,Roman Jaeschke,Elie A. Akl,Susan L. Norris,Gunn Elisabeth Vist,Philipp Dahm,Vijay K. Shukla,Julian P. T. Higgins,Yngve Falck–Ytter,Holger J. Schünemann
标识
DOI:10.1016/j.jclinepi.2011.03.017
摘要
This article deals with inconsistency of relative (rather than absolute) treatment effects in binary/dichotomous outcomes. A body of evidence is not rated up in quality if studies yield consistent results, but may be rated down in quality if inconsistent. Criteria for evaluating consistency include similarity of point estimates, extent of overlap of confidence intervals, and statistical criteria including tests of heterogeneity and I(2). To explore heterogeneity, systematic review authors should generate and test a small number of a priori hypotheses related to patients, interventions, outcomes, and methodology. When inconsistency is large and unexplained, rating down quality for inconsistency is appropriate, particularly if some studies suggest substantial benefit, and others no effect or harm (rather than only large vs. small effects). Apparent subgroup effects may be spurious. Credibility is increased if subgroup effects are based on a small number of a priori hypotheses with a specified direction; subgroup comparisons come from within rather than between studies; tests of interaction generate low P-values; and have a biological rationale.
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