Wu Cai, Li Liang, Li Ruosha
Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Biostatistics, University of Texas School of Public Health, Houston, TX, USA.
Stat Methods Med Res. 2020 Nov;29(11):3179-3191. doi: 10.1177/0962280220921553. Epub 2020 May 18.
The cause-specific cumulative incidence function quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is of interest to dynamically update the predicted cumulative incidence function by incorporating the most recent biomarker as well as the cumulating longitudinal history. Motivated by a longitudinal cohort study of chronic kidney disease, we propose a framework for dynamic prediction of end stage renal disease using multivariate longitudinal biomarkers, accounting for the competing risk of death. The proposed framework extends the local estimation-based landmark survival modeling to competing risks data, and implies that a distinct sub-distribution hazard regression model is defined at each biomarker measurement time. The model parameters, prediction horizon, longitudinal history and at-risk population are allowed to vary over the landmark time. When the measurement times of biomarkers are irregularly spaced, the predictor variable may not be observed at the time of prediction. Local polynomial is used to estimate the model parameters without explicitly imputing the predictor or modeling its longitudinal trajectory. The proposed model leads to simple interpretation of the regression coefficients and closed-form calculation of the predicted cumulative incidence function. The estimation and prediction can be implemented through standard statistical software with tractable computation. We conducted simulations to evaluate the performance of the estimation procedure and predictive accuracy. The methodology is illustrated with data from the African American Study of Kidney Disease and Hypertension.
病因特异性累积发病率函数可通过竞争风险结局来量化个体特定的疾病风险。对于纵向收集的生物标志物数据，通过纳入最新的生物标志物以及累积的纵向病史来动态更新预测的累积发病率函数是很有意义的。受一项慢性肾脏病纵向队列研究的启发，我们提出了一个使用多变量纵向生物标志物动态预测终末期肾病的框架，同时考虑了死亡的竞争风险。所提出的框架将基于局部估计的标志性生存模型扩展到竞争风险数据，并意味着在每个生物标志物测量时间定义一个独特的子分布风险回归模型。模型参数、预测期、纵向病史和风险人群在标志性时间上允许变化。当生物标志物的测量时间间隔不规则时，预测时可能无法观察到预测变量。局部多项式用于估计模型参数，而无需明确推算预测变量或对其纵向轨迹进行建模。所提出的模型使得回归系数的解释简单明了，并且预测的累积发病率函数可以进行封闭形式的计算。估计和预测可以通过具有易于处理计算的标准统计软件来实现。我们进行了模拟以评估估计程序的性能和预测准确性。该方法通过非裔美国人肾脏疾病和高血压研究的数据进行了说明。
