Development and validation of a Bayesian survival model for inclusion body myositis.
Gorana CapkunJens SchmidtShubhro GhoshHarsh SharmaThomas ObadiaAna de VeraValery RissonBilly AmzalPublished in: Theoretical biology & medical modelling (2019)
For IBM patients, results suggest an increased risk of premature death compared with the general population of the same age and gender. In the absence of hard data, bridging modelling generated survival predictions by combining relevant information. The methodological principle would be applicable to the analysis of associations between disease characteristics and payer-relevant outcomes in progressive chronic and rare diseases. Studies with lifetime follow-up would be needed to confirm the modelling results.
Keyphrases
- end stage renal disease
- ejection fraction
- chronic kidney disease
- multiple sclerosis
- newly diagnosed
- free survival
- peritoneal dialysis
- electronic health record
- mental health
- prognostic factors
- type diabetes
- adipose tissue
- patient reported outcomes
- big data
- skeletal muscle
- metabolic syndrome
- deep learning
- glycemic control
- myasthenia gravis