Abstract
GİRİŞ ve AMAÇ: Bu çalışmanın amacı; akciğer kanseri verisini analiz etmek için en uygun sağkalım modelini seçim kriterleri ile değerlendirerek akciğer kanseri verisinin prognostik faktörlerini ortaya çıkarmaktır.
YÖNTEM ve GEREÇLER: Çalışmada Mustafa Kemal Üniversitesi Tıp Fakültesi Tıbbi Onkoloji Polikliniğinden 185 akciğer kanseri tanısı almış hastaya ait veriler geriye dönük olarak hasta dosyalarından elde edilmiştir. Bireylerin arasındaki heterojenliklerin değerlendirilmesi amacıyla farklı dağılımlara sahip zayıflık modelleri kurulmuştur. Modeller AIC ve BIC kriterlerine göre değerlendirilmiştir.
BULGULAR: Çalışmadaki akciğer kanserli hastalara ait medyan sağkalım süresi 11 ay (%95 güven aralığı 9.57-12.42) olarak elde edilmiştir. Temel hazard fonksiyonu loglogistik ve zayıflık dağılımı lognormal olan zayıflık modeli en iyi model olarak belirlenmiştir. Bu modele ait sonuçlar incelendiğinde, albumin değişkeninin akciğer kanseri hastaların ölüm riski üzerine etkisi istatistiksel olarak anlamlı bulunmuştur (p=0.018).
TARTIŞMA ve SONUÇ: Genellikle akciğer kanseri hastalarının yaşam süresini etkileyen değerlendirmeye alınamayan çevresel ve genetik faktörlerin etkisi vardır. Dolayısıyla, akciğer kanseri hastalarının yaşam süresini etkileyen faktörler değerlendirilirken bireyler arasındaki heterojenliklerden kaynaklanan zayıflık teriminin de dikkate alınması alınması gerekmektedir.
Keywords
References
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Abstract
OBJECTIVE:The aim of this study is to investigate the prognostic factors of lung cancer by evaluating the most appropriate survival model with a selection criteria.
MATERIAL AND METHODS:In the study, the data of 185 patients diagnosed with lung cancer from the Medical Oncology Outpatient Clinic of Mustafa Kemal University Faculty of Medicine were retrospectively obtained from the patient files. The frailty models with different distributions were used for evaluating the heterogeneity between patients. Model selections were made according to AIC and BIC criteria.
RESULTS:The median survival time of patients with lung cancer in the study was 11 months (95% confidence interval 9.57-12.42). The best frailty models’ frailty distribution was lognormal and the basic hazard function distribution was loglogistic. The best model results showed that, the effect of the albumin variable on the risk of death of lung cancer patients was statistically significant (p = 0.018).
CONCLUSIONS:Generally, environmental and genetic factors that affect the survival time of lung cancer patients can not be evaluated.Thus, the term of the frailty resulting from the heterogeneity of factors when assessing individuals influencing survival of patients with lung cancer should be taken into account.
Keywords
References
- 1.Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010;127:2893-917.
- 2.Savaş İ, Akkoçlu A, Göksel T, Yılmaz U, ve ark. Lung and Pleural Malignancies Working Group.Lung cancer diagnosis and treatment guideline. Turkish Thoracic Journal 2006; 7: 1-37)
- 3.Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries - Bray - - CA: A Cancer Journal for Clinicians - Wiley Online Library- September 2018.)
- 4.(TÜİK. “Death Cause Statistics, 2017” News Bulletin, Number:27620, 26 April 2018.)
- 5.(T. C. Health MinistryCancer Incidence ın turkey in 2004-2006 www.kanser.gov.tr. Available: 26.08.2020)
- 6.Mountain CF. Revisions in the International System for Staging Lung Cancer. Chest 1997;111:1710-7.)
- 7.(Cancer Facts and Figures 2003, in American Cancer Society, Surveillance Research. Atlanta, American Cancer Society 2003)
- 8.Shields TW. Surgical Treatment of nonsmall cell lung cancer. In: Shields TW, Reed EW (eds). General Thoracic Surgery. 5th edition. Philadelphia: Lippincott Williams & Wilkins; 2000. 1311-41)
- 9.Quint LE, Tummala S, Brisson LJ, et al. Distribution of distant metastases from newly diag-nosed non- smail cell lung cancer. Ann Thorac Surg 1996;62:246-50)
- 10. Ozge, P., Ahmet, D., Handan, A., Rian, D., & Hasan, K. (2020). Assessment of death risk of breast cancer patients with joint frailty models. Saudi Medical Journal, 41(5), 491-498.
- 11.Hanagal, D.D. (2011). Modelling Survival Data Using frailty Models. Boca Raton: Chapman & Hall/CRC.
- 12. Munda, M., Rotolo, F., & Legrand, C. (2012). Parfm: parametric frailty models in R. Journal of Statistical Software, 51(11), 1-20.
- 13. Ucal, M. Ş. (2006). A Brief Survey of Econometrics Model Selection Criteria ;7(2):41-46
- 14. The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications. Frank J. Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev and Bala G. Arshanapalli. © 2014 John Wiley & Sons, Inc. Published 2014 by John Wiley & Sons, Inc.pp. 400-403
- 15.Değirmencioğlu, S. (2011). Angiogenic and prognostic impacts ofvascular endothelial growth factor, endothelin-1 and alpha-calcitonin gene-related peptide serum levels in advanced non-smallcell lung cancer patients,thesis, 2011, Pamukkale Unıversıty, medıcal schooldepartment of ınternal dıseases medıcal oncology,
- 16.Yang, J. R., Xu, J. Y., Chen, G. C., Yu, N., Yang, J., Zeng, D. X., ... & Qin, L. Q. (2019). Post-diagnostic C-reactive protein and albumin predict survival in Chinese patients with non-small cell lung cancer: a prospective cohort study. Scientific reports, 9(1), 8143.)
Details
| Primary Language | English |
|---|---|
| Subjects | Health Care Administration |
| Journal Section | Original Research |
| Authors | |
| Publication Date | December 30, 2020 |
| Acceptance Date | December 14, 2020 |
| Published in Issue | Year 2020 Volume: 10 Issue: 4 |
