Survival analysis in advanced lung cancer using Weibull survival regression model: estimation, interpretation, and clinical application

Authors

  • Ibrahim Abubakar Sadiq Department of Statistics, Ahmadu Bello University, Zaria. Nigeria https://orcid.org/0000-0002-2122-9344
  • Jibril Yahaya Kajuru Department of Statistics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
  • Sani Ibrahim Doguwa Department of Statistics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
  • Suleiman Suleiman Yahaya Department of Statistics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
  • Yahaya Yahaya Gambo Department of Statistics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
  • Ajayi Ayodamola Hephzibah Department of Statistics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
  • Samuel Wisdom Department of Statistics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
  • Mannir Isiya Department of Statistics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria
  • Abubakar Bello Department of Mathematics, Gombe State University, Gombe 760253, Nigeria

DOI:

https://doi.org/10.64497/jssci.30

Keywords:

Lung cancer, Survival analysis, Weibull regression, Log-Weibull regression, ECOG performance score, Karnofsky performance score, Hazard ratio, Kaplan-Meier estimator, Prognostic factors, Advanced cancer

Abstract

In this study, both Weibull and log-Weibull (Gumbel) survival regression models were applied to investigate the impact of key demographic and clinical factors on survival outcomes using a dataset of 228 patients from the North Central Cancer Treatment Group (NCCTG). The response variable was survival time (in days), while the covariates included age group, gender, ECOG performance score, Karnofsky performance score, calorie intake, and weight loss. The Weibull regression model demonstrated superior goodness-of-fit based on AIC (), BIC (), and log-likelihood values compared to the log-Weibull model. It revealed that ECOG performance status was among the most significant predictors of survival. Patients who were bedridden at least  of the day had a hazard ratio of  (95% CI: 1.426-89.448, p = 0.0217), indicating a markedly increased risk of mortality. Similarly, being symptomatic but ambulatory (HR = 1.62, p = 0.0168) and spending less than  of the day in bed (HR = 3.342, p < 0.001) were strongly associated with poorer survival outcomes. Female gender was significantly linked to better survival (HR = 0.558, 95% CI: 0.398–0.783, p = 0.0007), suggesting a protective effect in this cohort. While the Karnofsky score did not show strong statistical significance in multivariate modelling (p = 0.0302), Kaplan-Meier curves indicated a trend toward worse survival for patients with poor functional status. Age group did not emerge as a statistically significant predictor after adjusting for other variables. Weight loss over six months exhibited borderline significance (HR = 0.988, p = 0.0666), highlighting its potential influence on prognosis.

Downloads

Download data is not yet available.

References

1. American Cancer Society. (2021). Lung cancer survival rates. Link to Source

2. Armenian, H. K. (1987). Incubation Periods of Cancer: old and new. Journal of chronic diseases, 40, 9S-15S. DOI: https://doi.org/10.1016/S0021-9681(87)80004-8

3. Barlow, W. E., & Ichikawa, L. (2000). A utility-based approach to health-related quality of life assessment in cancer. Health and Quality of Life Outcomes, 1(1), 5.

4. Sadiq, A. I., Doguwa, S. I., Yahaya, A., & Garba, J. (2023). New Generalized Odd Fréchet-G (NGOF-G) Family of Distribution with Statistical Properties and Applications. UMYU Scientifica, 2(3), 100–107. https://doi.org/10.56919/usci.2323.016. DOI: https://doi.org/10.56919/usci.2323.016

5. Usman , . A. ., Doguwa, S. I. ., Sadiq, I. A. ., & Akor, A. (2025). Exploring Accelerated Failure Time Models for Tuberculosis Survival: Loglogistic and Weibull Survival Regression Model. Journal of Science Research and Reviews, 2(1), 27-36. https://doi.org/10.70882/josrar.2025.v2i1.27. DOI: https://doi.org/10.70882/josrar.2025.v2i1.27

6. Sadiq, I. A., Doguwa, S. I., Yahaya, A., & Garba, J. (2022). New odd Fréchet-G family of distribution with statistical properties and applications. AFIT Journal of Science and Engineering Research, 2(2), 84-103.

7. Gorgoso-Varela, J.J., Rojo-Alboreca, A. (2014). Use of Gumbel and Weibull functions to Model Extreme Values of Diameter Distributions in Forest Stands. Annals of Forest Science 71, 741–750 (2014). https://doi.org/10.1007/s13595-014-0369-1 DOI: https://doi.org/10.1007/s13595-014-0369-1

8. Jatoi, I., & Miller, B. E. (2018). The Weibull distribution in cancer survival data. Journal of Oncology Practice, 14(4), 192-198.

9. Kalbfleisch, J. D., & Prentice, L. M. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI: https://doi.org/10.1002/9781118032985

10. Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481. DOI: https://doi.org/10.1080/01621459.1958.10501452

11. Karnofsky, D. A., & Burchenal, J. H. (1949). The clinical evaluation of chemotherapeutic agents in cancer. In Evaluation of Chemotherapeutic Agents (pp. 191-205). Columbia University Press.

12. Klein, J. P., & Moeschberger, M. L., (2003.). Survival Analysis: Techniques for Censored and Truncated Data, Springer Science+Business Media, New York, NY, USA, DOI: https://doi.org/10.1007/b97377

13. Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer. DOI: https://doi.org/10.1007/978-1-4419-6646-9

14. Semary, H., Sadiq, I. A., Doguwa, S. I. S., Ishaq, A. I., Suleiman, A. A., Daud, H., & Abd Elgawad, M. A. (2025). Advancing survival regression using the NGOF exponentiated Weibull distribution for vesicovaginal fistula and radiation data applications. Journal of Radiation Research and Applied Sciences, 18(2), 101497. https://doi.org/10.1016/j.jrras.2025.101497 DOI: https://doi.org/10.1016/j.jrras.2025.101497

15. National Cancer Institute. (2021). Cancer Stat Facts: Lung Cancer. Link to Source

16. Oken, M. M., Creech, R. H., Tormey, D. C., Horton, J., Davis, T. E., McFadden, E. T., & Carbone, P. P. (1982). Toxicity and response criteria of the Eastern Cooperative Oncology Group. American journal of clinical oncology, 5(6), 649-656. DOI: https://doi.org/10.1097/00000421-198212000-00014

17. Pacheco, M. L., & David, C. R. (2017). The role of diet in lung cancer. Current Opinion in Oncology, 29(1), 61-65.

18. Peto, R., & Lee, P. (1973). Weibull distributions for Continuous-carcinogenesis Experiments. Biometrics, 457-470. DOI: https://doi.org/10.2307/2529169

19. Pike, M. C. (1966). A method of Analysis of a certain class of Experiments in Carcinogenesis. Biometrics, 22(1), 142-161. DOI: https://doi.org/10.2307/2528221

20. Schiller, J. H., Harrington, D., Belani, C. P., Langer, C., Sandler, A., Krook, J., & Johnson, D. H. (2002). Comparison of four chemotherapy regimens for advanced non-small-cell lung cancer. New England Journal of Medicine, 346(2), 92-98.21. DOI: https://doi.org/10.1056/NEJMoa011954

21. Therneau, T. M., & Lumley, T. (2015). Package ‘survival’. R Top Doc, 128(10), 28-33.

22. Zhang, Z. (2016). Parametric Regression Model for Survival Data: Weibull regression model as an example. Ann Transl Med 2016;4(24):484. doi: 10.21037/atm.2016.08.45. DOI: https://doi.org/10.21037/atm.2016.08.45

Downloads

Published

2025-08-04

How to Cite

Sadiq, I. A., Kajuru, J. Y., Doguwa, S. I., Yahaya, S. S., Gambo, Y. Y., Hephzibah, A. A., … Bello, A. (2025). Survival analysis in advanced lung cancer using Weibull survival regression model: estimation, interpretation, and clinical application. Journal of Statistical Sciences and Computational Intelligence, 1(2), 106–123. https://doi.org/10.64497/jssci.30

Issue

Vol. 1 No. 2 (2025): Volume 1, Issue 2

Section

Articles

License

Copyright (c) 2025 Ibrahim Abubakar Sadiq, Jibril Yahaya Kajuru, Sani Ibrahim Doguwa, Suleiman Suleiman Yahaya, Yahaya Yahaya Gambo, Ajayi Ayodamola Hephzibah, Samuel Wisdom, Mannir Isiya, Abubakar Bello

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Views
  • Abstract 491
  • PDF 329

Similar Articles

1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.