Analysing electronic health records: The benefits of target trial emulation

Abstract

AimElectronic health records (EHRs) are increasingly used in effectiveness and safety research. However, these studies are often at risk of bias. This study demonstrates the relevance, and discusses challenges, of using target trial emulation to avoid bias, such as selection bias, immortal time bias and confounding when performing observational research with EHRs. MethodsTarget trial emulation can be used to identify and address some of the drawbacks of observational research in a systematic way. Potential sources of bias are identified by describing key components of an ideal randomized controlled trial and comparing this to the observational study actually performed. The methods were applied to assess treatment response to antidiabetic treatment using EHRs from patients with diabetes treated in secondary care. ResultsUsing target trial emulation ensured prevalent users were excluded and patients were not included based on information generally not available when initiating a clinical trial. Furthermore, applying these methods demonstrated how the number of records eligible for use can rapidly decrease. Hereafter, adjustments were performed to address potential sources of bias and it was shown that missing variables essential for adjustment can be an important issue. ConclusionsUsing target trial emulation, sources of selection bias and confounding were identified and adjusted for accordingly when analysing treatment response in patients with type 2 diabetes. However, when using EHR data to emulate a target trial, samples containing sufficient information on outcome measures and variables to adjust for confounding and selection bias are essential given the risk of missing data.