Belot Aurélien, Rondeau Virginie, Remontet Laurent, Giorgi Roch
Service de Biostatistique, Hospices Civils de Lyon, F-69495 Pierre-Bénite Cedex, France; Université de Lyon, F-69000 Lyon, France; Université Lyon I, Villeurbanne, F-69622, France; CNRS ; UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique Santé, Pierre-Bénite, F-69495, France; Département des Maladies Chroniques et Traumatismes, Institut de Veille Sanitaire, Saint-Maurice, F-94415, France.
Stat Med. 2014 Aug 15;33(18):3147-66. doi: 10.1002/sim.6140. Epub 2014 Mar 17.
In chronic diseases, such as cancer, recurrent events (such as relapses) are commonly observed; these could be interrupted by death. With such data, a joint analysis of recurrence and mortality processes is usually conducted with a frailty parameter shared by both processes. We examined a joint modeling of these processes considering death under two aspects: 'death due to the disease under study' and 'death due to other causes', which enables estimating the disease-specific mortality hazard. The excess hazard model was used to overcome the difficulties in determining the causes of deaths (unavailability or unreliability); this model allows estimating the disease-specific mortality hazard without needing the cause of death but using the mortality hazards observed in the general population. We propose an approach to model jointly recurrence and disease-specific mortality processes within a parametric framework. A correlation between the two processes is taken into account through a shared frailty parameter. This approach allows estimating unbiased covariate effects on the hazards of recurrence and disease-specific mortality. The performance of the approach was evaluated by simulations with different scenarios. The method is illustrated by an analysis of a population-based dataset on colon cancer with observations of colon cancer recurrences and deaths. The benefits of the new approach are highlighted by comparison with the 'classical' joint model of recurrence and overall mortality. Moreover, we assessed the goodness of fit of the proposed model. Comparisons between the conditional hazard and the marginal hazard of the disease-specific mortality are shown, and differences in interpretation are discussed.
在诸如癌症等慢性疾病中，经常会观察到复发事件（如复发）；这些事件可能会因死亡而中断。对于此类数据，通常会对复发和死亡过程进行联合分析，并使用一个由两个过程共享的脆弱性参数。我们从两个方面考虑死亡情况，对这些过程进行联合建模：“因所研究疾病导致的死亡”和“因其他原因导致的死亡”，这使得能够估计疾病特异性死亡风险。使用超额风险模型来克服确定死亡原因的困难（不可用或不可靠）；该模型无需死亡原因，但利用在一般人群中观察到的死亡风险，就能估计疾病特异性死亡风险。我们提出一种在参数框架内对复发和疾病特异性死亡过程进行联合建模的方法。通过共享的脆弱性参数来考虑两个过程之间的相关性。这种方法能够估计协变量对复发风险和疾病特异性死亡风险的无偏效应。通过不同场景的模拟对该方法的性能进行了评估。通过对一个基于人群的结肠癌数据集进行分析，该数据集包含结肠癌复发和死亡的观察结果，来说明该方法。通过与复发和总体死亡率的“经典”联合模型进行比较，突出了新方法的优势。此外，我们评估了所提出模型的拟合优度。展示了疾病特异性死亡的条件风险和边际风险之间的比较，并讨论了解释上的差异。
