Overall survival (OS) is the gold standard for assessing patient benefit and cost-effectiveness of new cancer drugs. However, it is often difficult to use OS as the primary endpoint in randomized clinical trials (RCTs) for patients with metastatic cancer due to multiple reasons. In recent years, progression-free survival (PFS) has increasingly been used as the primary endpoint in metastatic cancer RCTs to accelerate development. However, regulatory authorities often seek mature OS data for approval. Therefore, it is critical to determine the target time when OS data are expected to be mature for reliable statistical inference. Motivated by an advanced renal cell carcinoma (RCC) clinical trial, we develop and investigate different prediction models leveraging information from disease progression to improve target OS prediction times. We propose a multivariate joint modeling approach considering components of progression and OS and extend three models commonly used for association to be used for OS prediction. To the best of our knowledge, this is the first comprehensive statistical study exploring the prediction of OS using different levels of information on disease progression and illustrating these models using a real, complex dataset. Our findings have significant implications for OS prediction.

翻译：总生存期（OS）是评估肿瘤新药患者获益和成本效益的金标准。然而，由于多重原因，在转移性癌症患者的随机临床试验（RCTs）中难以将OS作为主要终点。近年来，无进展生存期（PFS）越来越多地被用作转移性癌症RCTs的主要终点以加速药物开发。但监管机构通常要求提交成熟的OS数据用于批准。因此，确定OS数据预期可达到成熟以实现可靠统计推断的目标时间至关重要。基于一项晚期肾细胞癌（RCC）临床试验，我们开发并研究了利用疾病进展信息改进OS目标预测时间的不同预测模型。我们提出了一种考虑进展与OS组分的多变量联合建模方法，并扩展了三种常用于关联分析的模型用于OS预测。据我们所知，这是首个利用不同层面疾病进展信息探索OS预测，并使用真实复杂数据集展示这些模型的综合性统计研究。我们的发现对OS预测具有重要启示。