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Modelling disease progression of iron deficiency anaemia (IDA) following oraliron supplement prescriptions is a prerequisite for evaluating thecost-effectiveness of oral iron supplements. Electronic health records (EHRs)from the Clinical Practice Research Datalink (CPRD) provide rich longitudinaldata on IDA disease progression in patients registered with 663 GeneralPractitioner (GP) practices in the UK, but they also create challenges instatistical analyses. First, the CPRD data are clustered at multi-levels (i.e.,GP practices and patients), but their large volume makes it computationallydifficult to implement estimation of standard random effects models formulti-level data. Second, observation times in the CPRD data are irregular andcould be informative about the disease progression. For example, shorter/longergap times between GP visits could be associated with deteriorating/improvingIDA. Existing methods to address informative observation times are mostly basedon complex joint models, which adds more computational burden. To tackle thesechallenges, we develop a computationally efficient approach to modellingdisease progression with EHRs data while accounting for variability atmulti-level clusters and informative observation times. We apply the proposedmethod to the CPRD data to investigate IDA improvement and treatmentintolerance following oral iron prescriptions in primary care of the UK.

Modelling disease progression with multi-level electronic health records  data and informative observation times: an application to treating iron  deficiency anaemia in primary care of the UK