TITLE:
Effects of Treatment Delays on Colorectal Cancer Survival
AUTHORS:
Percy Guzmán, Michael T. Halpern
KEYWORDS:
Colorectal Cancer, Treatment Delay, Survival Analysis, PLCO Trial, Cox Regression
JOURNAL NAME:
Journal of Cancer Therapy,
Vol.17 No.2,
February
3,
2026
ABSTRACT: Introduction: Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Understanding the influence of patient characteristics and treatment modalities on delays in care and related outcomes is crucial for optimizing treatment strategies and improving patient survival. Methods: A retrospective analysis of 2359 CRC patients from the NCI PLCO trial was conducted to assess the relationships between treatment initiation delays (i.e., waiting times from diagnosis to start of treatment) and mortality outcomes. Descriptive statistics, multivariable regression analyses, and Cox proportional hazards regression analyses were employed to analyze the data. Results: Patients who underwent resection with chemotherapy and those who received chemotherapy without resection, as well as patients with disabilities, exhibited a higher hazard ratio (HR) for mortality. Individuals who used ibuprofen showed a lower HR for mortality. Despite examining multiple waiting time predictors, no significant associations were found between these predictors and mortality risk. The model’s non-significant likelihood ratio chi-square and log-likelihood value suggested that it did not provide an adequate fit to the data. Discussion: This study highlights the importance of understanding the relationships between patient characteristics, treatment modalities, and their impact on patient outcomes in CRC patients. The lack of association between waiting time predictors and mortality risk may reflect the short time between diagnosis and treatment initiation in this study cohort. Conclusion: Studies examining potential impacts of treatment initiation delays on CRC outcomes may need to utilize data from cohorts with broader ranges of delays. Further research is needed to identify additional factors that may influence mortality risk and to optimize treatment strategies for this patient population.