The University of New South Wales, Centre for Big Data Research in Health, Kensington, NSW, Australia.
School of Human Sciences, The University of Western Australia, Crawley, Perth, WA, 6009, Australia.
BMC Med Res Methodol. 2023 Apr 26;23(1):104. doi: 10.1186/s12874-023-01926-4.
BACKGROUND: Rheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology. METHODS: We conducted and compared the results of two analyses of the association between our predictor variable (percentage change in body mass index [BMI] from baseline to four years) and two outcome variable domains of structure and pain in knee and hip osteoarthritis. These two outcome variable domains covered 26 different outcomes for knee and hip combined. In the first analysis (categorical analysis), percentage change in BMI was categorized as ≥ 5% decrease in BMI, < 5% change in BMI, and ≥ 5% increase in BMI, while in the second analysis (continuous analysis), it was left as a continuous variable. In both analyses (categorical and continuous), we used generalized estimating equations with a logistic link function to investigate the association between the percentage change in BMI and the outcomes. RESULTS: For eight of the 26 investigated outcomes (31%), the results from the categorical analyses were different from the results from the continuous analyses. These differences were of three types: 1) for six of these eight outcomes, while the continuous analyses revealed associations in both directions (i.e., a decrease in BMI had one effect, while an increase in BMI had the opposite effect), the categorical analyses showed associations only in one direction of BMI change, not both; 2) for another one of these eight outcomes, the categorical analyses suggested an association with change in BMI, while this association was not shown in the continuous analyses (this is potentially a false positive association); 3) for the last of the eight outcomes, the continuous analyses suggested an association of change in BMI, while this association was not shown in the categorical analyses (this is potentially a false negative association). CONCLUSIONS: Categorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it.
背景：风湿病学研究人员经常对连续预测变量进行分类。我们旨在展示这种做法如何改变风湿病学观察性研究的结果。
方法：我们对我们的预测变量（从基线到四年时体重指数 [BMI] 的百分比变化）与膝关节和髋关节骨关节炎的结构和疼痛两个结局变量领域之间的关联进行了两次分析，并比较了这两次分析的结果。这两个结局变量领域涵盖了膝关节和髋关节共 26 个不同的结局。在第一次分析（分类分析）中，BMI 的百分比变化被分为 BMI 下降≥5%、BMI 变化<5%和 BMI 增加≥5%，而在第二次分析（连续分析）中，BMI 作为连续变量保留。在两次分析（分类和连续）中，我们都使用具有逻辑链接函数的广义估计方程来研究 BMI 百分比变化与结局之间的关联。
结果：在调查的 26 个结局中的 8 个（31%）中，分类分析的结果与连续分析的结果不同。这些差异有三种类型：1）对于这 8 个结果中的 6 个，虽然连续分析显示了两个方向的关联（即 BMI 下降有一个影响，而 BMI 增加有相反的影响），但分类分析仅显示了 BMI 变化的一个方向的关联，而不是两个方向的关联；2）对于这 8 个结果中的另一个，分类分析提示 BMI 变化与关联，但在连续分析中没有显示这种关联（这可能是一个假阳性关联）；3）对于这 8 个结果中的最后一个，连续分析提示 BMI 变化与关联，但在分类分析中没有显示这种关联（这可能是一个假阴性关联）。
结论：对连续预测变量的分类会改变分析结果，并可能导致不同的结论；因此，风湿病学研究人员应避免这种做法。
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