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.
Highlights
The future in many scientific domains lies in devices recording continuously and in real-time biological, physical, behavioural or environmental information
We focus on the distinction between non-weight bearing postures (NWBP) on one hand and weight-bearing postures and dynamic activities on another hand, because these two latter positions have both to be encouraged during the rehabilitation process
The probability to be in a non-weight bearing posture (NWBP) and the agreement level between the MOX and the video assessments are displayed in Figure 2 over the one hour observation period
Summary
Kappa coefficients are defined in terms of population parameters to Vanbelle (2016). Let the random variable Yir express the classification of item i by observer r, that is, Yir = 1 if observer r (r = 1, 2) classifies a randomly selected item i of population I in category 1 and is equal to zero otherwise. Consider the random variable Zi = 1 − I(Yi1 , Yi2 ) expressing the disagreement between the two observers on the classification of item i, where I(, ) is the identity function. The definition of the kappa coefficients is based on independent Zi. when intensive longitudinal data are the basis for the estimation of the kappa coefficients, this assumption does not hold because the Zi exhibit serial correlation. We develop a methodology to model dependent kappas obtained from an intensive longitudinal study, on the ground of Vanbelle and Lesaffre (2015)
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