<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study builds on previous research indicating that breast cancer survival time is influenced by several underlying factors. The study covered a period of 10 years from January 2012 to December 2022, and 140 cases were considered within the study cohort. The study considered breast cancer patients from North East Nigeria. The methodologies used are Cox hazard proportional regression and Kaplan–Meier analysis. The mean patient survival time is 592.2 days, with an average age of 44.61 years, average number of children of 5, and mean weight difference of 1.95 kg while on treatment. Kaplan-Meier analysis and the log rank test were used to investigate how the various covariates affect survival time, and it was found that age and family history have significant effects on the survival time in the studied population. The p-value of 0.04 for radiotherapy indicates statistical significance, in contrast to other treatment options such as surgery (p-value 0.7), targeted therapy (p-value 0.7), and chemotherapy (p-value 0.6). Residual diagnostic analysis with a component for assessment of Variance Inflation Factors (VIF) was used to detect multicollinearity among the independent variables. A total of 60 events (deaths) occurred within the study period with a concordance value of 0.73, which indicates a moderate level of agreement. This implies that the model’s predictions align reasonably well with the observed outcomes.</p>
</abstract>

This study builds on previous research indicating that breast cancer survival time is influenced by several underlying factors. The study covered a period of 10 years from January 2012 to December 2022, and 140 cases were considered within the study cohort. The study considered breast cancer patients from North East Nigeria. The methodologies used are Cox hazard proportional regression and Kaplan–Meier analysis. The mean patient survival time is 592.2 days, with an average age of 44.61 years, average number of children of 5, and mean weight difference of 1.95 kg while on treatment. Kaplan-Meier analysis and the log rank test were used to investigate how the various covariates affect survival time, and it was found that age and family history have significant effects on the survival time in the studied population. The p-value of 0.04 for radiotherapy indicates statistical significance, in contrast to other treatment options such as surgery (p-value 0.7), targeted therapy (p-value 0.7), and chemotherapy (p-value 0.6). Residual diagnostic analysis with a component for assessment of Variance Inflation Factors (VIF) was used to detect multicollinearity among the independent variables. A total of 60 events (deaths) occurred within the study period with a concordance value of 0.73, which indicates a moderate level of agreement. This implies that the model’s predictions align reasonably well with the observed outcomes.

Survival Analysis of Covariates Influencing Breast Cancer Treatment: A Case Study of North Eastern Nigeria

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This study builds on previous research indicating that breast cancer survival time is influenced by several underlying factors. The study covered a period of...

Survival Analysis of Covariates Influencing Breast Cancer Treatment: A Case Study of North Eastern Nigeria

Published Online: Jul 29, 2024

Page range: 1 - 15

DOI: https://doi.org/10.2478/bile-2024-0001

Keywords
breast cancer, survival time, multicollinearity, Cox hazard, Kaplan–Meier analysis

© 2024 Shadrach Yusuf et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Survival Analysis of Covariates Influencing Breast Cancer Treatment: A Case Study of North Eastern Nigeria

Published Online: Jul 29, 2024

Page range: 1 - 15

DOI: https://doi.org/10.2478/bile-2024-0001

Keywordsbreast cancer, survival time, multicollinearity, Cox hazard, Kaplan–Meier analysis

© 2024 Shadrach Yusuf et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
breast cancer, survival time, multicollinearity, Cox hazard, Kaplan–Meier analysis