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Joint models in big data: simulation-based guidelines for required data quality in longitudinal electronic health
Berit Hunsdieck1,2, Christian Bender3, Katja Ickstadt4,5
1Computational Biology, Bayer AG, Wuppertal, Germany. berit.hunsdieck@bayer.com.
Biodata Mining
|May 13, 2025
Summary
High-quality electronic health record (EHR) data are crucial for joint models. Simulations show that increased measurement frequency and reduced noise in EHR data improve joint model performance over traditional Cox models.
Area of Science:
- Biostatistics
- Health Informatics
- Clinical Epidemiology
Background:
- Electronic health records (EHR) are increasingly used in healthcare.
- Challenges exist regarding EHR data completeness and quality.
- The impact of data quality on complex models remains unclear.
Purpose of the Study:
- To provide simulation-based guidelines for EHR data quality for joint models.
- To determine conditions under which joint models outperform Cox models.
- To assess the influence of data quality characteristics on model performance.
Main Methods:
- Focus on joint models combining longitudinal and survival data.
- Conducted extensive simulations varying data quality (measurement frequency, noise, heterogeneity).
- Compared performance of joint models against traditional Cox survival models.
Main Results:
- Biomarker changes should be consistent within patient groups before disease onset.
- Joint models outperform Cox models with increased noise and measurement density.
- Guidelines illustrated with real-world examples (liver cirrhosis, chronic kidney disease).
Conclusions:
- Data quality significantly impacts the performance of joint models.
- Specific data quality characteristics can enhance joint model superiority over Cox models.
- Simulation-based guidelines can inform optimal use of EHR data in survival analysis.
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