When continuous outcomes are measured using different scales: guide for meta-analysis and interpretation
When continuous outcomes are measured using different scales: guide for meta-analysis and interpretation
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DOI：
10.1136/bmj.k4817
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发表时间：
2019-01-22
影响因子：
105.7
通讯作者：
Lin, Lifeng
中科院分区：
文献类型：
--
作者：
Murad, Mohammad Hassan;Wang, Zhen;Lin, Lifeng
通常使用不同的量表（如生活质量、焦虑或抑郁的严重程度）来测量连续的结果，因此这些结果需要在荟萃分析之前进行标准化。常用的标准化方法包括使用标准化平均差、连续数据的比值比、最小重要差和均值比。使数据对最终用户更有意义的其他方法包括将标准化效果转换回原始尺度，并使用假设的基线风险将优势比转换为绝对效果。为了使这些方法有效，跨研究组合的量表或工具需要评估相同或类似的结构
It is common to measure continuous outcomes using different scales (eg, quality of life, severity of anxiety or depression), therefore these outcomes need to be standardized before pooling in a meta-analysis. Common methods of standardization include using the standardized mean difference, the odds ratio derived from continuous data, the minimally important difference, and the ratio of means. Other ways of making data more meaningful to end users include transforming standardized effects back to original scales and transforming odds ratios to absolute effects using an assumed baseline risk. For these methods to be valid, the scales or instruments being combined across studies need to have assessed the same or a similar construct