Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques.
Developing prediction models to estimate the risk of two survival outcomes both occurring: A comparison of techniques.
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开发预测模型来估计两种生存结果同时发生的风险：技术比较。
DOI：
10.1002/sim.9771
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发表时间：
2023
影响因子：
2
通讯作者：
Pate A
中科院分区：
文献类型：
--
作者：
Pate A
前言本研究考虑对两种生存结果都发生的时间的预测。方法我们考虑了五种分析方法：乘积(乘积边际风险)、双结果(直接对两个事件发生前的时间进行建模)、多状态模型(MSM)以及一系列的Copula和脆弱模型。我们评估了在不同的模拟数据情景、不同的结果流行率和残差关联量下的校准和辨别。模拟的重点是模型的错误说明和统计功率。使用来自临床实践研究数据链的数据，我们比较了在预测心血管疾病和2型糖尿病两种情况下的风险时的模型性能。在存在残差相关的情况下，乘积方法的校准很差。MSM和双结果模型对模型错误说明的影响最强，但在小样本量时由于过度拟合而性能下降，而Copula和Frailty模型不太容易受到影响。Copula和Frailty模型的性能高度依赖于底层数据结构。在临床实例中，在调整8个主要心血管危险因素时，乘积方法的校准很差。讨论我们推荐使用双结果方法来预测两种生存结果都发生的风险。它是对模型错误说明最可靠的，尽管它也最容易过度拟合。这个临床例子激励了本研究中所考虑的方法的使用。
IntroductionThis study considers the prediction of the time until two survival outcomes have both occurred. We compared a variety of analytical methods motivated by a typical clinical problem of multimorbidity prognosis.MethodsWe considered five methods: product (multiply marginal risks), dual‐outcome (directly model the time until both events occur), multistate models (msm), and a range of copula and frailty models. We assessed calibration and discrimination under a variety of simulated data scenarios, varying outcome prevalence, and the amount of residual correlation. The simulation focused on model misspecification and statistical power. Using data from the Clinical Practice Research Datalink, we compared model performance when predicting the risk of cardiovascular disease and type 2 diabetes both occurring.ResultsDiscrimination was similar for all methods. The product method was poorly calibrated in the presence of residual correlation. The msm and dual‐outcome models were the most robust to model misspecification but suffered a drop in performance at small sample sizes due to overfitting, which the copula and frailty model were less susceptible to. The copula and frailty model's performance were highly dependent on the underlying data structure. In the clinical example, the product method was poorly calibrated when adjusting for 8 major cardiovascular risk factors.DiscussionWe recommend the dual‐outcome method for predicting the risk of two survival outcomes both occurring. It was the most robust to model misspecification, although was also the most prone to overfitting. The clinical example motivates the use of the methods considered in this study.