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

Abstract

BackgroundAssociations 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.MethodsCandidate 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.ResultsPresence 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).ConclusionsFor 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.

Inclusion body myositis and dysphagia. Presentation, intervention and outcome at a swallowing clinic

“…These factors are considered to contribute to mortality in patients with inclusion body myositis. 3,12 Therefore, the diagnosis of dysphagia in this group and its subsequent assessment and treatment is important for the quality of life for patients with inclusion body myositis.…”

Differential Diagnoses of Inclusion Body Myositis

“…Estimated health care costs are significant and several co-morbidities, including hypertension, hyperlipidaemia and ischaemic heart disease have been associated with IBM 4 . Using a Bayesian survival model for IBM, an increased risk of premature death compared with the general population of the same age and gender has recently been suggested for IBM 5 .…”