School of Biomedical Engineering, University of Technology Sydney, Level 10, Building 11, City Campus, Broadway, NSW, 2007, Australia.
Garvan Institute of Medical Research, Sydney, NSW, Australia.
Osteoporos Int. 2024 Nov;35(11):1989-1998. doi: 10.1007/s00198-024-07224-z. Epub 2024 Aug 15.
To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death.
We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses.
During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index = - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index = - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk.
Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly.
确定在存在死亡竞争事件的情况下预测脆性骨折风险的最佳统计方法。
我们使用了来自 Dubbo 骨质疏松症流行病学研究的真实世界数据，该研究监测了 3035 名老年参与者的骨骼健康和死亡率。脆性骨折通过放射学确定。死亡率由州登记处确认。我们考虑了四种用于预测骨折风险的统计模型：（i）传统的 Cox 比例风险模型，（ii）病因特异性模型，（iii）Fine-Gray 亚分布模型，和（iv）多状态模型。这些模型分别在开发（原始样本的 60%）和验证（40%）子集中进行拟合和验证。通过区分和校准分析评估模型性能。
在中位随访 11.3 年（IQR：7.2，16.2）期间，开发队列中有 628 名患者（34.5%）发生骨折，630 名患者（34.6%）在没有骨折的情况下死亡。模型之间的区分度和 5 年预测性能均无显著差异，尽管传统模型倾向于高估骨折风险（校准大指数 = - 0.24；95%CI： - 0.43， - 0.06）。对于 10 年风险预测，多状态模型（校准大指数 = - 0.05；95%CI： - 0.20，0.10）优于病因特异性模型（- 0.23； - 0.30， - 0.08）、Fine-Gray 模型（- 0.31； - 0.46， - 0.16）和传统模型（- 0.54； - 0.70， - 0.39），这些模型显著高估了骨折风险。
对死亡竞争风险的调整对骨折的短期预测影响最小。然而，多状态模型对长期骨折风险的预测最为准确，因此应考虑用于高死亡率的老年人群的预测研究。骨折风险评估可能因死亡竞争事件而受到影响。本研究使用真实世界数据发现，多状态模型在骨折风险评估中优于当前的竞争风险方法。多状态模型被认为是老年人群预测研究的最佳统计方法。
