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Title: Informative observation in health data: Association of past level and trend with time to next measurement
Authors: Sperrin, Matthew
Petherick, Emily S.
Badrick, Ellena
Editors: Randell, R
Keywords: Informative observation
Longitudinal modelling
Observation processes
Issue Date: 2017
Publisher: © European Federation for Medical Informatics (EFMI) and IOS Press
Citation: SPERRIN, M., PETHERICK, E.S. and BADRICK, E., 2017. Informative observation in health data: Association of past level and trend with time to next measurement. IN: Randell, R. ... et al (eds). Informatics for Health: Connected Citizen-Led Wellness and Population Health. Amsterdam: IOS Press, pp. 261-265.
Abstract: In routine health data, risk factors and biomarkers are typically measured irregularly in time, with the frequency of their measurement depending on a range of factors – for example, sicker patients are measured more often. This is termed informative observation. Failure to account for this in subsequent modelling can lead to bias. Here, we illustrate this issue using body mass index measurements taken on patients with type 2 diabetes in Salford, UK. We modelled the observation process (time to next measurement) as a recurrent event Cox model, and studied whether previous measurements in BMI, and trends in the BMI, were associated with changes in the frequency of measurement. Interestingly, we found that increasing BMI led to a lower propensity for future measurements. More broadly, this illustrates the need and opportunity to develop and apply models that account for, and exploit, informative observation.
Description: This is an Open Access Article. It is published by IOS Press under the Creative Commons Attribution Non-Commerical 4.0 Licence (CC BY-NC 4.0). Full details of this licence are available at: http://creativecommons.org/licenses/by-nc/4.0/
Version: Published
DOI: 10.3233/978-1-61499-753-5-261
URI: https://dspace.lboro.ac.uk/2134/24913
Publisher Link: http://dx.doi.org/10.3233/978-1-61499-753-5-261
ISBN: 9781614997528
9781614997535
Appears in Collections:Published Articles (Sport, Exercise and Health Sciences)

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