1Na’awurti William Nyandaiti, 2Isaac Esbond Gongsin 2Yusuf Abbakar Mohammed
1School of Health Information Management, University of Maiduguri Teaching Hospital, Nigeria
2Department of Statistics, University of Maiduguri
*Corresponding author’s Email: nnyandaiti@yahoo.com, doi.org/10.55639/607.02010077
ABSTRACT
This study introduces the Topp-Leone Epsilon (TopLE) distribution as a flexible parametric model for lung cancer survival data from the North Central Cancer Treatment Group (NCCTG) trial (n = 228). Compared to the exponential, log-logistic, and log-normal models, the TopLE model achieved the lowest Akaike Information Criterion (AIC = 2234.9) and highest log likelihood (−1107.4), making it the best among the standard models used in survival analysis. The TopLE model is also shown to have the highest discriminative ability (C-index = 0.742), and the lowest error metrics (IBS = 0.118; RMSE = 0.054). When extended with clinical covariates, male patients exhibited 48% higher mortality risk (HR = 1.48; 95% CI: 1.12 – 1.97), while age showed a marginal effect (HR = 0.98; p-value ≈ 0.05). Despite relatively wide confidence intervals for certain parameters, the TopLE model provided improved fit and visual agreement with Kaplan-Meier estimates. These findings suggest that the TopLE model is a robust, yet computationally sensitive, alternative for modelling complex hazard shapes in oncology survival data.
Keywords:
Censoring,
Clinical covariates,
Flexsurv,
Kaplan-Meier,
Oncology.

