Application of parametric survival analysis to women patients with breast cancer at Jimma University Medical Center

Abstract

Public health systems in both industrialized and undeveloped countries continue to struggle with the worldwide problem of breast cancer. In sub-Saharan African countries, notably Ethiopia, it is the form of cancer that strikes women the most commonly. Despite the extreme difficulties, the causes of mortality in Ethiopia have not yet been identified. In addition, little study has been done in this area. Therefore, the major objective of this analysis was to pinpoint the factors that were most responsible for the decreased life expectancy of breast cancer patients at the University of Jimma Medical Center. 552 women who had been treated for breast cancer at Jimma University Medical Center between October 2018 and December 2022 were included in this study, which used a retrospective cohort study design and five-year follow-up data. The most frequent and widely used test for comparing the probability of survival curves between several categorical independent variables was the log-rank test. Next, semi-parametric methods for multivariable analysis using the Cox proportional hazards model were used. Furthermore, a parametric strategy that includes fully parametric survival models better achieves the goal of the analysis. Among covariate, age of patient (ϕ = 254.06; 95% CI (3.95, 7.13), P-value = 0.000), patient live in urban (ϕ = 0.84; 95% CI (-0.35,-0.00), P-value = 0.047), preexisting comorbidity (ϕ = 2.46; 95% CI (0.39, 1.41), P-value = 0.001), overweight women cancer patient (ϕ = 0.05; 95% CI(-4.41,-1.57), P-value = 0.000, positive Axillary Node status cancer patient (ϕ = 0.04; 95% CI(-4.45,-1.88), P-value = 0.000), both surgery and chemotropic baseline treatment patient (ϕ = 0.53; 95% CI(-1.12,-0.16), P-value = 0.009) significantly affected the survival of women breast cancer. Age of breast cancer patient, patient education level, place of residence, marital status, pre-existing comorbidity, axillary node status, estrogen receptor, tumor size, body mass index at diagnosis, stage of cancer, and baseline treatment were found to have a significant effect on time to survive for women with breast cancer at the University of Jimma Medical Center, Oromia region, Ethiopia. However, the covariate histologic grade, number of positive lymph nodes involved, and type of hormone used were insignificant to the survival of breast cancer patients.

Keywords: AFT model; Breast cancer; Estrogen receptor; Parametric survival; Stage of cancer.

Fig. 1

Survival plot for the time to survive breast cancer

Fig. 2

Hazard plot of time to survive of breast cancer

Fig. 3

Survival time by age group of patients

Fig. 4

Survival time by baseline treatments

Fig. 5

Survival time by stage of cancer