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.
- Sport, Exercise and Health Sciences
Published inInformatics for Health: Connected Citizen-Led Wellness and Population Health
Informatics for Health: Connected Citizen-Led Wellness and Population Health
Pages261 - 265
CitationSPERRIN, 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). Vol. 235: Informatics for Health: Connected Citizen-Led Wellness and Population Health. Amsterdam: IOS Press, pp. 261-265.
PublisherIOS Press © European Federation for Medical Informatics (EFMI) and IOS Press
VersionVoR (Version of Record)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by-nc/4.0/
NotesReprinted from Informatics for Health: Connected Citizen-Led Wellness and Population Health, Vol 235, Matthew Sperrin, Emily Petherick, Ellena Badrick, Informative observation in health data: Association of past level and trend with time to next measurement, pp. 261-265, Copyright (2017), with permission from IOS Press. The publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-753-5-261
Book seriesStudies in Health Technology and Informatics;235