Categorisation of continuous exposure variables revisited. A response to the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study
Categorisation of continuous exposure variables revisited. A response to the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study
复制标题
DOI：
10.1186/1471-2288-10-103
复制
发表时间：
2010-11-09
影响因子：
4
通讯作者：
Veierod, Marit B.
中科院分区：
文献类型：
--
作者：
Froslie, Kathrine F.;Roislien, Jo;Veierod, Marit B.
背景资料：虽然一般的统计建议是在统计分析中保持连续暴露变量的连续性，但分类仍然是医学研究中的常用方法。在高血糖和不良妊娠结局（HAPO）研究的最新论文中，在分析体重指数（BMI）对不良妊娠结局的影响时使用了体重指数（BMI）的分类。最低的类别，标记为“体重不足”，被用作参考category.Methods：本文件给出了一个总结的原因分类和方法的缺点，这种方法。我们还讨论了参考范畴的选择和替代分析。我们通过对HAPO paper.Results的结果进行重新分析，来验证我们的论点：对连续暴露数据进行分类会导致功率损失和其他方法学挑战。参考类别的选择不当可能导致更加缺乏精确性，并模糊风险估计的解释。HAPO研究中突出显示的优势比（OR）是最高BMI类别中的女性与最低BMI类别中的女性相比（“肥胖III级”与“体重不足”）的出生体重>第90百分位数的OR。这一估计值为OR = 4.55和OR = 3.52，采用两种不同的多元Logistic回归模型。当使用“正常体重”类别作为参考时，我们相应的估计值分别为OR = 2.03和OR = 1.62。此外，我们选择的参考类别也给了较窄的置信区间。总结：由于一些方法上的缺陷，应避免分类。应使用现代统计分析来分析连续接触数据，并探索非线性关系。如果对连续数据进行分类，则必须特别注意参考类别的选择。
Background: Although the general statistical advice is to keep continuous exposure variables as continuous in statistical analyses, categorisation is still a common approach in medical research. In a recent paper from the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study, categorisation of body mass index (BMI) was used when analysing the effect of BMI on adverse pregnancy outcomes. The lowest category, labelled "underweight", was used as the reference category.Methods: The present paper gives a summary of reasons for categorisation and methodological drawbacks of this approach. We also discuss the choice of reference category and alternative analyses. We exemplify our arguments by a reanalysis of results from the HAPO paper.Results: Categorisation of continuous exposure data results in loss of power and other methodological challenges. An unfortunate choice of reference category can give additional lack of precision and obscure the interpretation of risk estimates. A highlighted odds ratio (OR) in the HAPO study is the OR for birth weight > 90(th) percentile for women in the highest compared to the lowest BMI category ("obese class III" versus "underweight"). This estimate was OR = 4.55 and OR = 3.52, with two different multiple logistic regression models. When using the "normal weight" category as the reference, our corresponding estimates were OR = 2.03 and OR = 1.62, respectively. Moreover, our choice of reference category also gave narrower confidence intervals.Summary: Due to several methodological drawbacks, categorisation should be avoided. Modern statistical analyses should be used to analyse continuous exposure data, and to explore non-linear relations. If continuous data are categorised, special attention must be given to the choice of reference category.