Hughes David M, El Saeiti Riham, García-Fiñana Marta
Department of Biostatistics, University of Liverpool, Liverpool, UK.
Department of Statistics, University of Benghazi, Benghazi, Libya.
Biom J. 2018 Mar;60(2):307-322. doi: 10.1002/bimj.201700013. Epub 2017 Aug 21.
Longitudinal discriminant analysis (LoDA) can be used to classify patients into prognostic groups based on their clinical history, which often involves longitudinal measurements of various clinically relevant markers. Patients' longitudinal data is first modelled using multivariate generalised linear mixed models, allowing markers of different types (e.g. continuous, binary, counts) to be modelled simultaneously. We describe three approaches to calculating a patient's posterior group membership probabilities which have been outlined in previous studies, based on the marginal distribution of the longitudinal markers, conditional distribution and distribution of the random effects. Here we compare the three approaches, first using data from the Mayo Primary Biliary Cirrhosis study and then by way of simulation study to explore in which situations each of the three approaches is expected to give the best prediction. We demonstrate situations in which the marginal or random-effects approach perform well, but find that the conditional approach offers little extra information to the random-effects and marginal approaches.
纵向判别分析（LoDA）可用于根据患者的临床病史将其分类为不同的预后组，这通常涉及对各种临床相关标志物的纵向测量。首先使用多变量广义线性混合模型对患者的纵向数据进行建模，从而可以同时对不同类型的标志物（例如连续型、二元型、计数型）进行建模。我们描述了三种计算患者后验组成员概率的方法，这些方法在先前的研究中已有概述，它们基于纵向标志物的边际分布、条件分布和随机效应分布。在此，我们首先使用梅奥原发性胆汁性肝硬化研究的数据，然后通过模拟研究来比较这三种方法，以探究在哪些情况下这三种方法中的每一种有望给出最佳预测。我们展示了边际或随机效应方法表现良好的情况，但发现条件方法相对于随机效应和边际方法几乎没有提供额外信息。
