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

TL;DR

This paper introduces a computationally efficient statistical method for modeling disease progression using multi-level electronic health records data, accounting for irregular and informative observation times, demonstrated through an application to iron deficiency anaemia in UK primary care.

Contribution

The paper develops a novel, efficient approach for analyzing multi-level EHR data with informative observation times, addressing computational challenges in disease progression modeling.

Findings

Identified factors influencing IDA improvement post-treatment

Quantified treatment intolerance rates in primary care

Demonstrated method's effectiveness on large EHR dataset

Abstract

Modelling disease progression of iron deficiency anaemia (IDA) following oral iron supplement prescriptions is a prerequisite for evaluating the cost-effectiveness of oral iron supplements. Electronic health records (EHRs) from the Clinical Practice Research Datalink (CPRD) provide rich longitudinal data on IDA disease progression in patients registered with 663 General Practitioner (GP) practices in the UK, but they also create challenges in statistical analyses. First, the CPRD data are clustered at multi-levels (i.e., GP practices and patients), but their large volume makes it computationally difficult to implement estimation of standard random effects models for multi-level data. Second, observation times in the CPRD data are irregular and could be informative about the disease progression. For example, shorter/longer gap times between GP visits could be associated with deteriorating/improving IDA. Existing methods to address informative observation times are mostly based on complex joint models, which adds more computational burden. To tackle these challenges, we develop a computationally efficient approach to modelling disease progression with EHRs data while accounting for variability at multi-level clusters and informative observation times. We apply the proposed method to the CPRD data to investigate IDA improvement and treatment intolerance following oral iron prescriptions in primary care of the UK.