Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box 14155-6446, Tehran, Iran.
Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
Eur J Epidemiol. 2018 Jan;33(1):5-14. doi: 10.1007/s10654-017-0325-0. Epub 2017 Nov 3.
Misconceptions about the impact of case-control matching remain common. We discuss several subtle problems associated with matched case-control studies that do not arise or are minor in matched cohort studies: (1) matching, even for non-confounders, can create selection bias; (2) matching distorts dose-response relations between matching variables and the outcome; (3) unbiased estimation requires accounting for the actual matching protocol as well as for any residual confounding effects; (4) for efficiency, identically matched groups should be collapsed; (5) matching may harm precision and power; (6) matched analyses may suffer from sparse-data bias, even when using basic sparse-data methods. These problems support advice to limit case-control matching to a few strong well-measured confounders, which would devolve to no matching if no such confounders are measured. On the positive side, odds ratio modification by matched variables can be assessed in matched case-control studies without further data, and when one knows either the distribution of the matching factors or their relation to the outcome in the source population, one can estimate and study patterns in absolute rates. Throughout, we emphasize distinctions from the more intuitive impacts of cohort matching.
关于病例对照匹配影响的误解仍然很常见。我们讨论了几种与匹配病例对照研究相关的微妙问题，这些问题在匹配队列研究中不会出现或不太严重：（1）匹配，即使是非混杂因素，也可能产生选择偏差；（2）匹配会扭曲匹配变量与结果之间的剂量反应关系；（3）无偏估计需要考虑实际的匹配方案以及任何残留的混杂效应；（4）为了提高效率，应将完全匹配的组合并；（5）匹配可能会损害精度和功效；（6）即使使用基本的稀疏数据方法，匹配分析也可能受到稀疏数据偏差的影响。这些问题支持建议将病例对照匹配限制在少数几个经过良好测量的强混杂因素上，如果没有测量到这些混杂因素，则退化为不匹配。从积极的方面来看，可以在匹配病例对照研究中评估匹配变量对优势比的影响，而无需进一步的数据，并且当知道匹配因素的分布或它们与源人群中结果的关系时，可以估计和研究绝对比率的模式。在整个过程中，我们强调了与队列匹配更直观的影响的区别。
