Statistical Modelling 23 (2) (2023), 127–150

Modelling agreement for binary intensive longitudinal data

Sophie Vanbelle,
Department of Methodology and Statistics, CAPHRI,
Faculty of Health, Medicine and Life,
Sciences,
Maastricht University,
The Netherlands.
e-mail: sophie.vanbelle@maastrichtuniversity.nl

Emmanuel Lesaffre,
Leuven Biostatistics and Statistical Bioinformatics Centre,
KU Leuven,
Leuven,
Belgium.

Abstract:

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.

Keywords:

continuous recording, reliability, time-event sequential data, time series, transient event

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