Start Time End Time Integration (STETI): Analyzing Trends in Kidney Cancer Survival Time Data

Background/Objectives: Accurately estimating survival times for kidney cancer patients is critical for clinical decision-making, treatment evaluation, resource allocation other purposes. Yet data from relatively recent diagnosis cohorts presents an important difficulty: five, 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 understand in the present. The proposed approach is shown for kidney cancer survival but could be applied to survival problems connected to survival for other types of cancer, other diseases, stage progression times, and similar problems in medicine and engineering in which there is a need to understand trends of improvement in survival. Methods: This study introduces a novel method for survival estimation that addresses limitations in traditional approaches by incorporating recent survival data often excluded due to incomplete longitudinal records. Leveraging data from the SEER database resource, the proposed approach integrates historical diagnosis year cohorts with more recent death year cohorts. This permits survival time trend analyses that account for both earlier and more recent improvements in treatment effectiveness. We used linear and exponential models to demonstrate the method's ability to predict survival trends using valuable data that would otherwise risk being ignored. Conclusions: Better survival estimates can better support personalized treatment planning, health care benchmarking, and research into cancer subtypes as well as other domains. This hybrid analytical approach paves the way to applications in oncology and beyond, and offers a robust method for quantifying and predicting the survival trends associate with therapeutic advancements.