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Analyzing missingness patterns in real-world data using the SMDI toolkit: application to a linked EHR-claims
Sudha R Raman1, Bradley G Hammill2, Pamela A Shaw3
1Department of Population Health Sciences, Duke University School of Medicine, Durham, USA. Sudha.raman@duke.edu.
BMC Medical Research Methodology
|October 19, 2024
Summary
Missing data in real-world electronic health records (EHR) is a challenge. A new toolkit (SMDI) helped characterize missing data patterns and guide analysis, improving evidence generation from EHR data.
Area of Science:
- Real-world data analysis
- Pharmacoepidemiology
- Health informatics
Background:
- Missing data in confounding variables is a frequent challenge in real-world data (RWD) research, including electronic health records (EHR).
- Characterizing missing data patterns and understanding their mechanisms is crucial for robust evidence generation.
- This study applies a novel toolkit to address these challenges in an empirical case.
Purpose of the Study:
- To apply the Structural Missing Data Investigations (SMDI) toolkit for characterizing missing data patterns in a pharmacoepidemiology study.
- To illustrate the decision-making process for data analysis based on identified missingness mechanisms.
- To demonstrate the practical application of the SMDI toolkit in a real-world setting.
Main Methods:
- Utilized the SMDI toolkit to analyze missing data in a study comparing cardiovascular outcomes of SGLT2i and DPP-4i initiators.
- Focused on partially observed confounders (HbA1c, BMI) in a linked EHR-Medicare claims dataset.
- Employed SMDI's descriptive functions and diagnostic tests to explore missingness and inform mitigation strategies.
Main Results:
- High missingness observed for HbA1c (63.6%) and BMI (16.5%).
- Diagnostic tests provided insights into missingness patterns, covariate distributions, and predictability.
- Multiple imputation using chained equations with random forests effectively addressed missing confounder data, aligning effect estimates with prior research.
Conclusions:
- Successfully demonstrated the practical application of the SMDI toolkit in a real-world RWD setting.
- SMDI toolkit insights informed the selection of appropriate analytic methods for handling missing data.
- This approach enhances transparency and the ability to generate reliable evidence from RWD.
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