标题： 基于Z分数的方法及其在生物监测中的应用：对职业足球运动员和自行车运动员的扩展分析 显示英文标题

标题： Z-scores-based methods and their application to biological monitoring: An extended analysis of professional soccer players and cyclists athletes

摘要： 生物数据的收集增加使得可以对血液学或尿液生物标志物进行个体和纵向监测。 然而，识别这些生物序列中的异常行为并不简单。 此外，生物数据的复杂性（生物标志物之间的相关性、季节性影响等）也是一个问题。 Z分数方法可以帮助评估这些纵向序列中的异常性，同时捕捉一些生物复杂性的特征。 本研究详细描述了一个统计框架，使用三种自定义的Z分数方法在个体内部变异范围内处理生物序列。 这些方法可以根据季节性、时间顺序或生物标志物之间的相关性检测纵向序列中的异常样本。 其中一种方法是对一种自定义Z分数方法的扩展，应用于高斯线性模型，这允许在模型设计中包含额外变量。 我们通过3,936名职业足球运动员（5个生物标志物）和1,683名业余或职业自行车运动员（10个生物标志物）的纵向数据来说明该框架的应用。 结果表明，一种专门设计用于检测一系列连续观察值变化的Z分数方法，在两个数据集的铁蛋白和IGF1生物标志物中测得了较高比例的异常值（超过三倍的假阳性率）。 所提出的框架和方法可以应用于其他情境，例如在临床患者随访中监测生物标志物的异常值。 这些方法足够灵活，可以包含更复杂的生物特征，这些特征可以直接纳入模型设计中。 显示英文摘要

摘要： The increase in the collection of biological data allows for the individual and longitudinal monitoring of hematological or urine biomarkers. However, identifying abnormal behavior in these biological sequences is not trivial. Moreover, the complexity of the biological data (correlation between biomarkers, seasonal effects, etc.) is also an issue. Z-score methods can help assess the abnormality in these longitudinal sequences while capturing some features of the biological complexity. This work details a statistical framework for handling biological sequences using three custom Z-score methods in the intra-individual variability scope. These methods can detect abnormal samples in the longitudinal sequences with respect to the seasonality, chronological time or correlation between biomarkers. One of these methods is an extension of one custom Z-score method to the Gaussian linear model, which allows for including additional variables in the model design. We illustrate the use of the framework on the longitudinal data of 3,936 professional soccer players (5 biomarkers) and 1,683 amateur or professional cyclists (10 biomarkers). The results show that a particular Z-score method, designed to detect a change in a series of consecutive observations, measured a high proportion of abnormal values (more than three times the false positive rate) in the ferritin and IGF1 biomarkers for both data sets. The proposed framework and methods could be applied in other contexts, such as the clinical patient follow-up in monitoring abnormal values of biological markers. The methods are flexible enough to include more complicated biological features, which can be directly incorporated into the model design.