  Electronic health records (EHRs) provide an efficient approach to generating
rich longitudinal datasets. However, since patients visit as needed, the
assessment times are typically irregular and may be related to the patient's
health. Failing to account for this informative assessment process could result
in biased estimates of the disease course. In this paper, we show how
estimation of the disease trajectory can be enhanced by leveraging an
underutilized piece of information that is often in the patient's EHR:
physician-recommended intervals between visits. Specifically, we demonstrate
how recommended intervals can be used in characterizing the assessment process,
and in investigating the sensitivity of the results to assessment not at random
(ANAR). We illustrate our proposed approach in a clinic-based cohort study of
juvenile dermatomyositis (JDM). In this study, we found that the recommended
intervals explained 78% of the variability in the assessment times. Under a
specific case of ANAR where we assumed that a worsening in disease led to
patients visiting earlier than recommended, the estimated population average
disease activity trajectory was shifted downward relative to the trajectory
assuming assessment at random. These results demonstrate the crucial role
recommended intervals play in improving the rigour of the analysis by allowing
us to assess both the plausibility of the AAR assumption and the sensitivity of
the results to departures from this assumption. Thus, we advise that studies
using irregular longitudinal data should extract recommended visit intervals
and follow our procedure for incorporating them into analyses.
