当前位置: X-MOL 学术Theor. Biol. Med. Model. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Development and validation of a Bayesian survival model for inclusion body myositis.
Theoretical Biology and Medical Modelling ( IF 2.432 ) Pub Date : 2019-11-07 , DOI: 10.1186/s12976-019-0114-4
Gorana Capkun 1 , Jens Schmidt 2 , Shubhro Ghosh 3 , Harsh Sharma 3 , Thomas Obadia 4 , Ana de Vera 1 , Valery Risson 1 , Billy Amzal 5
Affiliation  

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.

中文翻译:

包涵体肌炎的贝叶斯生存模型的开发和验证。

背景技术对于具有稀疏可用数据的罕见和进行性慢性疾病,可能难以建立疾病特征与付款人相关结果之间的关联。我们开发了一种探索性桥接模型,以根据疾病特征预测过早死亡,并使用包涵体肌炎(IBM)作为代表性案例研究。方法疾病专家和IBM文献确定了可能与过早死亡相关的候选变量。使用现有的患者水平数据评估了IBM患者中候选变量之间的相互依赖性。使用确定的变量作为模型中过早死亡的预测变量,开发了IBM人群的贝叶斯生存模型。对于模型选择和外部验证,将模型预测与IBM患者队列中已发布的死亡率数据进行比较。验证之后,将最终模型用于模拟IBM患者过早死亡的风险增加。基线生存率是根据世界卫生组织报告的西方国家普通人群的年龄和性别特定的生存曲线得出的。结果根据疾病专家和文献提供的输入,吞咽困难,吸入性肺炎,跌倒,轮椅束缚和6分钟步行距离(6兆瓦时,以米为单位)的存在被确定为候选变量,可以用作预测过早死亡的指标。这些功能绩效指标之间的相关性有限,因此在模型中被视为独立变量。根据贝叶斯生存模型,在所有候选变量中,吞咽困难和6MWD [m]的降低与较差的生存率相关,危险比(HR)为1.61(95%可信区间[CrI]:0.84-3.50)和2.48(95%CrI:1.27-5.00)。在IBM队列与年龄和性别匹配的普通人群队列中模拟的超额死亡率为4.03(95%的预测区间为1.37-10.61)。结论对于IBM患者,与同年龄和性别的普通人群相比,过早死亡的风险增加。在没有硬数据的情况下,桥接建模通过组合相关信息来生成生存预测。该方法学原理将适用于分析进行性慢性和罕见病中疾病特征与付款人相关结局之间的关联。
更新日期:2019-11-01
down
wechat
bug