Araştırma Makalesi
BibTex RIS Kaynak Göster

THE USE OF COX REGRESSION MODEL IN THE SURVIVAL ANALYSIS FOR LEUKEMIA PATIENTS IN THE REPUBLIC OF YEMEN

Yıl 2023, Cilt: 24 Sayı: 3, 192 - 206, 22.09.2023

Elıas Abdullah Al-samaı Sevil Şentürk

https://doi.org/10.18038/estubtda.1335496

Öz

This study aims at analyzing and studying the theoretical and practical importance of the (Cox) regression model in the analysis of survival as well as measuring the most important factors affecting the survival time for patients with leukemia. Moreover, it aims at reaching the expected survival time for patients and creating a life table for patients by using (Cox) regression model. To achieve these goals, real data were taken for (1168) patients with leukemia in the Republic of Yemen in the period from January 2017 to February 2022. The dependent variable, which is the patient’s condition at the end of the period, was determined in addition to the patient’s survival time and eight independent variables were identified. The effect of these variables on the survival time of patients with leukemia was investigated using the SPSS program. The study concluded several results, the most prominent of them are the following: There are no differences in the incidence rate between males and females and the most age group affected by this disease is (40 years and over). Furthermore, it was found that acute lymphoblastic leukemia (ALL) is the most prevalent type among the other types and there is a difference in the risk of death among those who take intravenous chemotherapy and those patients who take oral chemotherapy. Other significant result was found that there is a higher risk of death for non-regular patients in receiving treatment compared to regular patients in receiving treatment. It was found that the most influencing variables on the survival time of patients are (age, marital status, type of disease, the governorate in which the patient lives, regularity in receiving treatment and type of chemotherapy). Through the life table, it is noticed that the greatest risk in the survival time is in the thirty-sixth month, which is the largest among all other periods, as it reached (0.10). Additionally, the median survival time was reached (35.06) months. Finally, the study found that there are differences in the incidence according to the type of disease in terms of the risk of death as acute lymphoblastic leukemia (ALL) is the most prevalent disease among all diseases and the largest percentage of deaths was among those with chronic myeloid leukemia (CML).

Anahtar Kelimeler

Cox Regression Survival Analysis Censored Event

Kaynakça

  • [1] Tabachnick BG, Fidell LS, Ullman JB. Using Multivariate Statistics. Pearson Boston, MA; 2007.
  • [2] Liu ST. SAS , Survival Analysis Techniques for Medical Research; 2004.
  • [3] Walstra P, Wouters JTM, Geurts TJ. Survival analysis a practical approach. Dairy science & Technology, CRC Taylor & Francis Group. Published online 2005:267.
  • [4] Lee ET, Wang J. Statistical Methods for Survival Data Analysis. John Wiley & Sons; 2003.
  • [5] Alrun MB. Survival Analysis of the Registered Colorectal Cancer Cases in the Gaza Strip. BMC Public Health. Published online 2017.
  • [6] Marshall AW, Olkin I. Life Distributions. Springer; 2007.
  • [7] Okal M. Survival analysis of breast cancer patients in Gaza Strip. Published online 2010.
  • [8] Kamal SMM. Socio-economic determinants of age at first marriage of the ethnic tribal women in Bangladesh. Asian Population Studies. 2011;7(1):69-84.
  • [9] Burcu Küley Ağir. Survival and cox regression analyzes: A Case Study From Animals Science. Published Online 2017.
  • [10] Selim S, Sülükçüler S. Duration analysis of factors affecting smoking time: A Case Study of Türkiye. Bağımlılık Dergisi. 2023;24(4):475-486.
  • [11] Çilengiroğlu Öv. Evaluation of the first job finding periods of university graduates with cox regression model. Türkiye Sosyal Araştırmalar Dergisi. 2023;27(1):49-68.
  • [12] Klein JP, Moeschberger ML. Survival Analysis: Techniques for Censored and Truncated Data. Springer Science & Business Media; 2006.
  • [13] Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association. 1958;53(282):457-481.
  • [14] Allison PD. Survival Analysis Using SAS: A Practical Guide. Second Edi. Sas Institute; 2010.
  • [15] Cox DR. Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological). 1972;34(2):187-202.
  • [16] O’Quigley J. Proportional Hazards Regression. Springer; 2008.
  • [17] Klein JP. Handbook of Survival Analysis.; 2016.
  • [18] McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The Bulletin Of Mathematical Biophysics. 1943;5(4):115-133.
  • [19] Walters SJ. What Is a Cox Model? Citeseer; 2009.
  • [20] Lawless JF. Statistical Models and Methods for Lifetime Data. John Wiley & Sons; 2011.

THE USE OF COX REGRESSION MODEL IN THE SURVIVAL ANALYSIS FOR LEUKEMIA PATIENTS IN THE REPUBLIC OF YEMEN

Yıl 2023, Cilt: 24 Sayı: 3, 192 - 206, 22.09.2023

Elıas Abdullah Al-samaı Sevil Şentürk

https://doi.org/10.18038/estubtda.1335496

Öz

This study aims at analyzing and studying the theoretical and practical importance of the (Cox) regression model in the analysis of survival as well as measuring the most important factors affecting the survival time for patients with leukemia. Moreover, it aims at reaching the expected survival time for patients and creating a life table for patients by using (Cox) regression model. To achieve these goals, real data were taken for (1168) patients with leukemia in the Republic of Yemen in the period from January 2017 to February 2022. The dependent variable, which is the patient’s condition at the end of the period, was determined in addition to the patient’s survival time and eight independent variables were identified. The effect of these variables on the survival time of patients with leukemia was investigated using the SPSS program. The study concluded several results, the most prominent of them are the following: There are no differences in the incidence rate between males and females and the most age group affected by this disease is (40 years and over). Furthermore, it was found that acute lymphoblastic leukemia (ALL) is the most prevalent type among the other types and there is a difference in the risk of death among those who take intravenous chemotherapy and those patients who take oral chemotherapy. Other significant result was found that there is a higher risk of death for non-regular patients in receiving treatment compared to regular patients in receiving treatment. It was found that the most influencing variables on the survival time of patients are (age, marital status, type of disease, the governorate in which the patient lives, regularity in receiving treatment and type of chemotherapy). Through the life table, it is noticed that the greatest risk in the survival time is in the thirty-sixth month, which is the largest among all other periods, as it reached (0.10). Additionally, the median survival time was reached (35.06) months. Finally, the study found that there are differences in the incidence according to the type of disease in terms of the risk of death as acute lymphoblastic leukemia (ALL) is the most prevalent disease among all diseases and the largest percentage of deaths was among those with chronic myeloid leukemia (CML).

Anahtar Kelimeler

Cox Regression Survival Analysis Censored Event

Kaynakça

  • [1] Tabachnick BG, Fidell LS, Ullman JB. Using Multivariate Statistics. Pearson Boston, MA; 2007.
  • [2] Liu ST. SAS , Survival Analysis Techniques for Medical Research; 2004.
  • [3] Walstra P, Wouters JTM, Geurts TJ. Survival analysis a practical approach. Dairy science & Technology, CRC Taylor & Francis Group. Published online 2005:267.
  • [4] Lee ET, Wang J. Statistical Methods for Survival Data Analysis. John Wiley & Sons; 2003.
  • [5] Alrun MB. Survival Analysis of the Registered Colorectal Cancer Cases in the Gaza Strip. BMC Public Health. Published online 2017.
  • [6] Marshall AW, Olkin I. Life Distributions. Springer; 2007.
  • [7] Okal M. Survival analysis of breast cancer patients in Gaza Strip. Published online 2010.
  • [8] Kamal SMM. Socio-economic determinants of age at first marriage of the ethnic tribal women in Bangladesh. Asian Population Studies. 2011;7(1):69-84.
  • [9] Burcu Küley Ağir. Survival and cox regression analyzes: A Case Study From Animals Science. Published Online 2017.
  • [10] Selim S, Sülükçüler S. Duration analysis of factors affecting smoking time: A Case Study of Türkiye. Bağımlılık Dergisi. 2023;24(4):475-486.
  • [11] Çilengiroğlu Öv. Evaluation of the first job finding periods of university graduates with cox regression model. Türkiye Sosyal Araştırmalar Dergisi. 2023;27(1):49-68.
  • [12] Klein JP, Moeschberger ML. Survival Analysis: Techniques for Censored and Truncated Data. Springer Science & Business Media; 2006.
  • [13] Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association. 1958;53(282):457-481.
  • [14] Allison PD. Survival Analysis Using SAS: A Practical Guide. Second Edi. Sas Institute; 2010.
  • [15] Cox DR. Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological). 1972;34(2):187-202.
  • [16] O’Quigley J. Proportional Hazards Regression. Springer; 2008.
  • [17] Klein JP. Handbook of Survival Analysis.; 2016.
  • [18] McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. The Bulletin Of Mathematical Biophysics. 1943;5(4):115-133.
  • [19] Walters SJ. What Is a Cox Model? Citeseer; 2009.
  • [20] Lawless JF. Statistical Models and Methods for Lifetime Data. John Wiley & Sons; 2011.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Uygulamalı İstatistik
Bölüm Makaleler
Yazarlar

Elıas Abdullah Al-samaı 0000-0003-0663-0305

Sevil Şentürk 0000-0002-9503-7388

Yayımlanma Tarihi 22 Eylül 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 24 Sayı: 3

Kaynak Göster

AMA Al-samaı EA, Şentürk S. THE USE OF COX REGRESSION MODEL IN THE SURVIVAL ANALYSIS FOR LEUKEMIA PATIENTS IN THE REPUBLIC OF YEMEN. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. Eylül 2023;24(3):192-206. doi:10.18038/estubtda.1335496