Evidence-based Practice Center, Mayo Clinic, Rochester, MN 55902, USA
Evidence-based Practice Center, Mayo Clinic, Rochester, MN 55902, USA.
BMJ. 2019 Jan 22;364:k4817. doi: 10.1136/bmj.k4817.
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
在元分析中对不同量表（如生活质量、焦虑或抑郁严重程度）测量的连续结局进行汇总前，通常需要对其进行标准化处理。常见的标准化方法包括使用标准化均数差、源于连续性数据的优势比、最小临床重要差值和均数比。使数据对终端用户更有意义的其他方法包括将标准化效应转换回原始量表，以及使用假设的基线风险将优势比转换为绝对效应。为了使这些方法有效，需要保证跨研究组合的量表或工具评估的是相同或相似的结构。
