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Modelling agreement for binary intensive longitudinal data
Statistical Modelling ( IF 1.2 ) Pub Date : 2021-09-03 , DOI: 10.1177/1471082x211034002
Sophie Vanbelle 1 , Emmanuel Lesaffre 2
Affiliation  

Devices that measure our physical, medical and mental condition have entered our daily life recently. Such devices measure our status in a continuous manner and can be useful in predicting future medical events or can guide us towards a healthier life. It is therefore important to establish that such devices record our behaviour in a reliable manner and measure what we believe they measure. In this article, we propose to measure the reliability and validity of a newly developed measuring device in time using a longitudinal model for sequential kappa statistics. We propose a Bayesian estimation procedure. The method is illustrated by a validation study of a new accelerometer in cardiopulmonary rehabilitation patients.



中文翻译:

二进制密集纵向数据的建模协议

最近,测量我们身体、医疗和精神状况的设备已经进入我们的日常生活。此类设备以连续方式测量我们的状态,可用于预测未来的医疗事件或引导我们走向更健康的生活。因此,重要的是要确定此类设备以可靠的方式记录我们的行为并测量我们认为它们测量的内容。在本文中,我们建议使用连续 kappa 统计的纵向模型及时测量新开发的测量设备的可靠性和有效性。我们提出了一个贝叶斯估计程序。该方法通过对心肺康复患者的新型加速度计进行的验证研究来说明。

更新日期:2021-09-03
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