Shi Haolun, Yin Guosheng
Department of Statistics and Actuarial Science, The University of Hong Kong, Lung Fu Shan, Hong Kong.
Stat Methods Med Res. 2017 Oct;26(5):2042-2054. doi: 10.1177/0962280217708681. Epub 2017 Jun 19.
We propose a class of landmark cure rate models by incorporating time-dependent covariates. The landmark approach enables us to obtain dynamic predictions of a patient's survival probabilities as new clinical information emerges during the follow-up time. The proposed method extends the landmark model for failure time data with a possible cure fraction. We specify a series of landmark time points, and for each of time point, we construct a landmark data set consisting of only those at-risk individuals at the landmark time. The time-dependent covariates can be fixed at the values evaluated at the landmark time point. Through landmarking, our framework accommodates the Cox proportional hazards model, accelerated failure time model and censored quantile regression model in the presence of a cure proportion. We conduct simulation studies to assess the estimation accuracy of the proposed method, and illustrate it with data from a heart transplant study.
我们通过纳入随时间变化的协变量，提出了一类标志性治愈率模型。标志性方法使我们能够在随访期间有新的临床信息出现时，获得患者生存概率的动态预测。所提出的方法扩展了具有可能治愈比例的失效时间数据的标志性模型。我们指定一系列标志性时间点，对于每个时间点，我们构建一个仅由在该标志性时间处于风险中的个体组成的标志性数据集。随时间变化的协变量可以固定在标志性时间点评估的值上。通过标志性方法，我们的框架在存在治愈比例的情况下纳入了Cox比例风险模型、加速失效时间模型和删失分位数回归模型。我们进行模拟研究以评估所提出方法的估计准确性，并用心脏移植研究的数据进行说明。
