Development and validation of a Bayesian survival model for inclusion body myositis

Autor: V Risson, Gorana Capkun, Harsh Sharma, Billy Amzal, Thomas Obadia, Shubhro Ghosh, Ana de Vera, Jens Schmidt
Přispěvatelé: Novartis Pharma AG, University Medical Center Göttingen (UMG), LASER ANALYTICA, Paris (LA-SER), Certara, This work was funded by Novartis.
Jazyk: angličtina
Rok vydání: 2019
Předmět:
media_common.quotation_subject
Population
Health Informatics
Disease
lcsh:Computer applications to medicine. Medical informatics
Bridging
Models
Biological
Chronic disease
Myositis
Inclusion Body
Cohort Studies
03 medical and health sciences
0302 clinical medicine
Confidence Intervals
Credible interval
Humans
Medicine
030212 general & internal medicine
education
lcsh:QH301-705.5
Survival analysis
media_common
education.field_of_study
Variables
business.industry
Research
Hazard ratio
Reproducibility of Results
Prediction interval
Bayes Theorem
Prognosis
Survival modelling
Survival Analysis
3. Good health
lcsh:Biology (General)
Modeling and Simulation
Bayesian model
Cohort
lcsh:R858-859.7
Inclusion body myositis
Deglutition Disorders
business
Rare disease
030217 neurology & neurosurgery
[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Demography
Zdroj: Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling, BioMed Central, 2019, 16 (1), pp.17. ⟨10.1186/s12976-019-0114-4⟩
Theoretical Biology and Medical Modelling, Vol 16, Iss 1, Pp 1-10 (2019)
Theoretical Biology & Medical Modelling
ISSN: 1742-4682
DOI: 10.1186/s12976-019-0114-4⟩
Popis: Background Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study. Methods Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature. Interdependency between candidate variables in IBM patients were assessed using existing patient-level data. A Bayesian survival model for the IBM population was developed with identified variables as predictors for premature mortality in the model. For model selection and external validation, model predictions were compared to published mortality data in IBM patient cohorts. After validation, the final model was used to simulate the increased risk of premature death in IBM patients. Baseline survival was based on age- and gender-specific survival curves for the general population in Western countries as reported by the World Health Organisation. Results Presence of dysphagia, aspiration pneumonia, falls, being wheelchair-bound and 6-min walking distance (6MWD in meters) were identified as candidate variables to be used as predictors for premature mortality based on inputs received from disease experts and literature. There was limited correlation between these functional performance measures, which were therefore treated as independent variables in the model. Based on the Bayesian survival model, among all candidate variables, presence of dysphagia and decrease in 6MWD [m] were associated with poorer survival with contributing hazard ratios (HR) 1.61 (95% credible interval [CrI]: 0.84–3.50) and 2.48 (95% CrI: 1.27–5.00) respectively. Excess mortality simulated in an IBM cohort vs. an age- and gender matched general-population cohort was 4.03 (95% prediction interval 1.37–10.61). Conclusions 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.
Databáze: OpenAIRE
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