Cardiac Intensive Care Unit, The Royal Children's Hospital, Melbourne, Victoria, Australia.
Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
Cardiol Young. 2023 Aug;33(8):1337-1341. doi: 10.1017/S1047951122002384. Epub 2022 Aug 4.
In medical research, continuous variables are often categorised into two or more groups before being included in the analysis; this practice often comes with a cost, such as loss of power in analysis, less reliable estimates, and can often leave residual confounding in the results. In this research report, we show this by way of estimates from a regression analysis looking at the association between acute kidney injury and post-operative mortality in a sample of 194 neonates who underwent the Norwood operation. Two models were developed, one using a continuous measure of renal function as the main explanatory variable and second using a categorised version of the same variable. A continuous measure of renal function is more likely to yield reliable estimates and also maintains more statistical power in the analysis to detect a relation between the exposure and outcome. It also reveals the true biological relationship between the exposure and outcome. Categorising a continuous variable may not only miss an important message, it can also get it wrong. Additionally, given a non-linear relationship is commonly encountered between the exposure and outcome variable, investigators are advised to retain a predictor with a linear term only when supported by data. All of this is particularly important in small data sets which account for the majority of clinical research studies.
在医学研究中，连续变量通常在纳入分析之前被分为两个或更多组；这种做法通常会带来一些代价，例如分析效能降低、估计值不太可靠，并且往往会导致结果中仍然存在残余混杂因素。在本研究报告中，我们通过对 194 名接受 Norwood 手术的新生儿样本中急性肾损伤与术后死亡率之间关联的回归分析结果来说明这一点。我们建立了两个模型，一个使用肾功能的连续度量作为主要解释变量，第二个使用相同变量的分类版本。连续的肾功能度量更有可能产生可靠的估计值，并且在分析中保持更多的统计效能，以检测暴露与结果之间的关系。它还揭示了暴露与结果之间的真实生物学关系。对连续变量进行分类不仅可能会遗漏重要信息，而且还可能会得出错误的结论。此外，鉴于暴露与结果变量之间通常存在非线性关系，建议调查人员仅在数据支持的情况下保留具有线性项的预测因子。所有这些在小数据集（占大多数临床研究的数据集）中尤为重要。
