Given the increasing prevalence of adult obesity, furthering understanding of the determinants of measures such as the body mass index (BMI ) remains high on the policy agenda. We contribute to existing literature on modelling the BMI by proposing an extension to latent class modelling, which serves to unveil a more detailed picture of the determinants of BMI. Interest here lies in latent class analysis with a regression model and predictor variables explaining class membership, a regression model and predictor variables explaining the outcome variable within BMI classes and instances where the BMI classes are naturally ordered and labelled by expected values within class. A simple and generic way of parameterizing both the class probabilities and the statistical representation of behaviours within each class is proposed, that simultaneously preserves the ranking according to class‐specific expected values and yields a parsimonious representation of the class probabilities. Based on a wide range of metrics, the newly proposed approach is found to dominate the prevailing approach and, moreover, results are often quite different across the two.

中文翻译：

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潜在类别建模的新方法：识别各种类型的体重指数个体

鉴于成人肥胖的患病率日益上升，对诸如体重指数（BMI）之类的决定因素的进一步了解仍然是政策议程上的重点。我们提议对潜在类建模进行扩展，从而为有关BMI建模的现有文献做出贡献，这有助于揭示有关BMI决定因素的更多详细信息。这里的兴趣在于潜在类分析，其具有解释模型成员资格的回归模型和预测变量，解释BMI类中结果变量的回归模型和预测变量以及BMI类自然排序并由类中的期望值标记的实例。提出了一种简单且通用的参数化类概率和每个类中行为的统计表示的方法，该方法同时根据类特定的期望值保留排名，并产生类概率的简约表示。基于广泛的指标，发现新提出的方法在主流方法中占主导地位，此外，两者之间的结果通常大不相同。