Departement of Primary Care & Population Health, King's College London, London, U.K.
Stat Med. 2012 Nov 20;31(26):3089-103. doi: 10.1002/sim.5354. Epub 2012 Aug 2.
Dichotomisation of continuous data is known to be hugely problematic because information is lost, power is reduced and relationships may be obscured or changed. However, not only are differences in means difficult for clinicians to interpret, but thresholds also occur in many areas of medical practice and cannot be ignored. In recognition of both the problems of dichotomisation and the ways in which it may be useful clinically, we have used a distributional approach to derive a difference in proportions with a 95% CI that retains the precision and the power of the CI for the equivalent difference in means. In this way, we propose a dual approach that analyses continuous data using both means and proportions to replace dichotomisation alone and that may be useful in certain situations. We illustrate this work with examples and simulations that show good performance of the parametric approach under standard distributional assumptions from our own research and from the literature.
将连续数据二值化是有问题的，因为这样会丢失信息、降低功效，并且可能扭曲或改变关系。然而，均值的差异不仅让临床医生难以解释，而且在医学实践的许多领域都存在阈值，不能被忽视。鉴于二值化的问题及其在临床上可能有用的方式，我们使用了一种分布方法来推导具有 95%CI 的比例差异，该方法保留了等效均值差异的 CI 的精度和功效。通过这种方式，我们提出了一种双重方法，使用均值和比例来分析连续数据，以替代单独的二值化，并在某些情况下可能有用。我们用来自我们自己的研究和文献的示例和模拟来说明这种方法，这些示例和模拟显示了参数方法在标准分布假设下的良好性能。
