| Abstract [eng] |
Survival analysis and mortality modeling are key methodologies in medical research that provide a sound basis for analyzing data and understanding the factors that influence patient death. This study examines survival in the Lithuanian population after a diagnosis of gastric, breast, or prostate cancer. It analyses data by age, sex, calendar year, and stage of disease to determine their impact on survival rates. Methods such as Kaplan ‐ Meier curves, the Cox proportional hazards model, the Lee ‐ Carter prediction method, and competing risk models are used to improve understanding of the determinants of survival. This work is distinguished by the application of less explored methods, such as the Lee ‐ Carter model and competing risks analysis, which can provide unique insights into the prognosis of patient survival after a cancer diagnosis. Comparing the results of the models allows for a more precise identification of the influence of age, which emerges as one of the most important factors. The results have important practical implications for policymakers to allocate healthcare resources more efficiently, develop prevention and early diagnosis programs, and improve treatment strategies for better patient survival outcomes. |
