Accurate survival predicting models are essential for improving targeted cancer therapies and clinical care among cancer patients. In this article, we investigate and develop a method to improve predictions of survival in cancer by leveraging two-phase data with expert knowledge and prognostic index. Our work is motivated by two-phase data in nasopharyngeal cancer (NPC), where traditional covariates are readily available for all subjects, but the primary viral factor, Human Papillomavirus (HPV), is substantially missing. To address this challenge, we propose an expert guided method that incorporates prognostic index based on the observed covariates and clinical importance of key factors. The proposed method makes efficient use of available data, not simply discarding patients with unknown HPV status. We apply the proposed method and evaluate it against other existing approaches through a series of simulation studies and real data example of NPC patients. Under various settings, the proposed method consistently outperforms competing methods in terms of c-index, calibration slope, and integrated Brier score. By efficiently leveraging two-phase data, the model provides a more accurate and reliable predictive ability of survival models.

翻译：精确的生存预测模型对于改善癌症患者的靶向治疗和临床护理至关重要。本文研究并开发了一种方法，通过结合专家知识和预后指数利用两阶段数据来改进癌症生存预测。我们的研究受鼻咽癌两阶段数据的启发：所有受试者的传统协变量均易于获取，但主要病毒因素——人乳头瘤病毒（HPV）存在大量缺失。为应对这一挑战，我们提出了一种专家引导方法，该方法结合了基于观测协变量的预后指数及关键因素的临床重要性。所提方法能有效利用现有数据，而非简单丢弃HPV状态未知的患者。我们通过一系列模拟研究和鼻咽癌患者的真实数据案例，应用所提方法并与现有其他方法进行比较评估。在不同设定下，所提方法在c指数、校准斜率和综合Brier分数方面均持续优于其他竞争方法。通过高效利用两阶段数据，该模型为生存模型提供了更准确可靠的预测能力。