Chaduka Thobani, Berleant Daniel, Bauer Michael A, Tsai Peng-Hung, Tu Shi-Ming
Department of Information Science, University of Arkansas at Little Rock, 2801 S. University Ave., Little Rock, AR 72204, USA.
College of Medicine, University of Arkansas for Medical Sciences, 4301 W. Markham St., Little Rock, AR 72205, USA.
Healthcare (Basel). 2025 Jun 17;13(12):1451. doi: 10.3390/healthcare13121451.
Accurately estimating survival times is critical for clinical decision-making, treatment evaluation, resource allocation, and other purposes. Yet data from relatively recent diagnosis cohorts is strongly affected by right censoring that biases average survival times downward. For example, 5-, 10-, or 20-year survival time averages are not available until 5, 10, or 20 years later, which may be in the future, thus presenting a challenge to obtain in the present. An approach to addressing this problem is described in this report. Here it is demonstrated for kidney cancer survival but could also be applied to survival questions for other types of cancer, other diseases, stage progression times, and similar problems in medicine and other fields in which there is a need for up-to-date analyses of survival improvement trends. : This study introduces STETI, an approach to survival estimation that integrates information about survival times of diagnosis year cohorts with information about survival times of death year cohorts. By leveraging data from death year cohorts in addition to the more familiar diagnosis year cohorts, STETI incorporates recent survival data often excluded by traditional approaches due to right censoring, caused when the post-diagnosis time period of interest has not yet elapsed. Using data from SEER, we explain how the proposed approach integrates diagnosis year cohorts with the death year cohorts of recent years. We demonstrate that incorporating death year cohorts addresses an important source of right censorship that is inherent in diagnosis year cohorts from relatively recent years. This permits survival time trend analysis that accounts for recent improvements in survival time that would be difficult to account for using diagnosis year cohorts alone. We tested linear and exponential models to demonstrate the method's ability to derive survival time trends using valuable data that would otherwise risk being left unused. : Improved survival estimation can better support personalized treatment planning, healthcare benchmarking, and research into cancer subtypes as well as other domains. To this end, we introduce a hybrid analytical approach that addresses an important source of right censorship. Demonstrating it within the domain of kidney cancer is expected to help pave the way to other applications in oncology and beyond, and offers a case study of STETI, an approach to quantifying and projecting trends in survival time associated with therapeutic advancements.
准确估计生存时间对于临床决策、治疗评估、资源分配及其他目的至关重要。然而，来自相对近期诊断队列的数据受到右删失的强烈影响，这会使平均生存时间向下偏倚。例如，5年、10年或20年的平均生存时间要到5年、10年或20年后才可得，而这可能还在未来，从而给当前获取这些数据带来挑战。本报告描述了一种解决此问题的方法。这里以肾癌生存为例进行了演示，但也可应用于其他类型癌症、其他疾病、疾病进展阶段时间以及医学和其他领域中需要对生存改善趋势进行最新分析的类似问题。：本研究引入了STETI，一种生存估计方法，它将诊断年份队列的生存时间信息与死亡年份队列的生存时间信息整合在一起。通过除了更熟悉的诊断年份队列之外利用死亡年份队列的数据，STETI纳入了传统方法通常因右删失而排除的近期生存数据，右删失是在感兴趣的诊断后时间段尚未过去时产生的。利用监测、流行病学和最终结果（SEER）的数据，我们解释了所提出的方法如何将诊断年份队列与近年来的死亡年份队列整合。我们证明纳入死亡年份队列解决了相对近期诊断年份队列中固有的右删失的一个重要来源。这使得能够进行生存时间趋势分析，该分析考虑了近期生存时间的改善，而仅使用诊断年份队列则难以做到这一点。我们测试了线性和指数模型，以证明该方法利用否则可能会被闲置的有价值数据得出生存时间趋势的能力。：改进的生存估计可以更好地支持个性化治疗计划、医疗保健基准制定以及对癌症亚型及其他领域的研究。为此，我们引入了一种混合分析方法，该方法解决了右删失的一个重要来源。在肾癌领域内对其进行演示有望为肿瘤学及其他领域的其他应用铺平道路，并提供了一个STETI的案例研究，STETI是一种量化和预测与治疗进展相关的生存时间趋势的方法。
