Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease.
Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease.
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DOI：
10.1186/s12874-018-0592-9
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发表时间：
2018-11-16
影响因子：
4
通讯作者：
Mills JA
中科院分区：
文献类型：
--
作者：
Long JD;Mills JA
联合建模是适当的，当一个人想要预测的时间与纵向测量的协变量，并与事件相关的事件。一个潜在的随机效应结构将生存和纵向子模型联系起来，并允许个体特定的预测。可以包括多个时变和时不变协变量，以潜在地提高预测精度。本研究的目的是估计多变量联合模型的几个纵向观察研究的亨廷顿氏病，检查外部效度性能，并计算个体特异性预测表征疾病进展。重点是预测运动诊断风险的生存子模型。分析了来自四项观察性研究的数据：Enroll-HD、PREDICT-HD、REGIONAL和Track-HD。采用贝叶斯方法进行估计，并使用时变AUC测量进行外部验证。基于模拟方法计算个体特定累积危害预测。累积风险用于计算运动发作的预测年龄，也用于表示观察到的诊断状态和基于模型的状态之间的差异的偏差残差。在单个研究中训练的联合模型在其余测试研究中区分诊断和预诊断参与者方面具有非常好的性能，5年平均AUC = .83（范围.77-.90），10年平均AUC = .86（范围.82-.92）。对运动诊断的预测年龄的图形分析显示，与引起亨廷顿病的三核苷酸扩增有预期的密切关系。偏差型残差的图形分析显示，尽管具有相对较低的基于模型的风险，但仍有一些人转换为诊断，其他人尽管具有相对较高的风险，但尚未转换，大多数人介于两个极端之间。联合建模是对传统生存建模的改进，因为它考虑了预测事件的协变量的所有纵向观察结果。来自联合模型的预测可以具有更高的准确性，因为它们是针对个体差异而定制的。这些预测可以提供个体疾病进展的相对准确的表征，这在干预的时机、确定适当临床试验的资格和一般基因型分析中可能是重要的。
Joint modeling is appropriate when one wants to predict the time to an event with covariates that are measured longitudinally and are related to the event. An underlying random effects structure links the survival and longitudinal submodels and allows for individual-specific predictions. Multiple time-varying and time-invariant covariates can be included to potentially increase prediction accuracy. The goal of this study was to estimate a multivariate joint model on several longitudinal observational studies of Huntington’s disease, examine external validity performance, and compute individual-specific predictions for characterizing disease progression. Emphasis was on the survival submodel for predicting the hazard of motor diagnosis. Data from four observational studies was analyzed: Enroll-HD, PREDICT-HD, REGISTRY, and Track-HD. A Bayesian approach to estimation was adopted, and external validation was performed using a time-varying AUC measure. Individual-specific cumulative hazard predictions were computed based on a simulation approach. The cumulative hazard was used for computing predicted age of motor onset and also for a deviance residual indicating the discrepancy between observed diagnosis status and model-based status. The joint model trained in a single study had very good performance in discriminating among diagnosed and pre-diagnosed participants in the remaining test studies, with the 5-year mean AUC = .83 (range .77–.90), and the 10-year mean AUC = .86 (range .82–.92). Graphical analysis of the predicted age of motor diagnosis showed an expected strong relationship with the trinucleotide expansion that causes Huntington’s disease. Graphical analysis of the deviance-type residual revealed there were individuals who converted to a diagnosis despite having relatively low model-based risk, others who had not yet converted despite having relatively high risk, and the majority falling between the two extremes. Joint modeling is an improvement over traditional survival modeling because it considers all the longitudinal observations of covariates that are predictive of an event. Predictions from joint models can have greater accuracy because they are tailored to account for individual variability. These predictions can provide relatively accurate characterizations of individual disease progression, which might be important in the timing of interventions, determining the qualification for appropriate clinical trials, and general genotypic analysis.
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影响因子：
2
作者：
Antolini, L;Boracchi, P;Biganzoli, E
通讯作者：
Biganzoli, E
影响因子：
2.1
作者：
Henderson, R;Diggle, P;Dobson, A
通讯作者：
Dobson, A
影响因子：
5.4
作者：
Cologne, John;Hsu, Wan-Ling;Cullings, Harry M.
通讯作者：
Cullings, Harry M.
影响因子：
4
作者：
Collins GS;de Groot JA;Dutton S;Omar O;Shanyinde M;Tajar A;Voysey M;Wharton R;Yu LM;Moons KG;Altman DG
通讯作者：
Altman DG
影响因子：
2
作者：
Crowther, Michael J.;Andersson, Therese M-L.;Humphreys, Keith
通讯作者：
Humphreys, Keith