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首页> 外文期刊>Journal of applied statistics >Screening for prostate cancer using multivariate mixed-effects models
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Screening for prostate cancer using multivariate mixed-effects models

机译：使用多元混合效应模型筛查前列腺癌

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Using several variables known to be related to prostate cancer, a multivariate classification method is developed to predict the onset of clinical prostate cancer. A multivariate mixed-effects model is used to describe longitudinal changes in prostate-specific antigen (PSA), a free testosterone index (FTI), and body mass index (BMI) before any clinical evidence of prostate cancer. The patterns of change in these three variables are allowed to vary depending on whether the subject develops prostate cancer or not and the severity of the prostate cancer at diagnosis. An application of Bayes' theorem provides posterior probabilities that we use to predict whether an individual will develop prostate cancer and, if so, whether it is a high-risk or a low-risk cancer. The classification rule is applied sequentially one multivariate observation at a time until the subject is classified as a cancer case or until the last observation has been used. We perform the analyses using each of the three variables individually, combined together in pairs, and all three variables together in one analysis. We compare the classification results among the various analyses and a simulation study demonstrates how the sensitivity of prediction changes with respect to the number and type of variables used in the prediction process.
机译：使用已知与前列腺癌相关的几个变量，开发了一种多元分类方法来预测临床前列腺癌的发作。多元混合效应模型用于描述前列腺癌的任何临床证据之前的前列腺特异性抗原（PSA），游离睾丸激素指数（FTI）和体重指数（BMI）的纵向变化。这三个变量的变化模式可以根据受试者是否患有前列腺癌以及诊断时前列腺癌的严重程度而有所不同。贝叶斯定理的应用提供了后验概率，我们可以用来预测一个人是否会患上前列腺癌，如果是这样，则是高危还是低危癌症。一次将一个多变量观察顺序应用分类规则，直到将受试者分类为癌症病例或直到使用了最后一个观察为止。我们分别使用三个变量中的每个变量进行分析，成对组合在一起，一次分析中将所有三个变量组合在一起。我们将各种分析之间的分类结果进行比较，并且仿真研究证明了预测的敏感性如何随预测过程中使用的变量的数量和类型而变化。

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