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.
Keywords: Informative observation; Longitudinal modelling; Observation processes.