Publication:

Accelerated failure time survival model for prostate cancer

Background Prostate cancer is the second most common cancer in men globally and ranks sixth among male cancer-related deaths. This study utilized survival analysis to assess post-diagnosis outcomes, aiming to enhance prostate cancer patient longevity. Insights were gained by determining median survival times, identifying prognostic factors, and applying an AFT model to prostate cancer data.

Methods The study collected data from 502 prostate cancer patients featured in a 1980 paper by D. P. Byar and S. B. Green, focusing on stage 3 or 4 cases. Over six years, they monitored these patients' survival under varying estrogen doses, analyzing 15 variables. Data import was done using excel, R and SPSS were used for the analysis. Utilizing the Kaplan-Meier method and AFT models, the study identified predictive factors and determined the most suitable model for prostate cancer analysis.

Results Among the 502 patients, 70.7% died, and 29.3% were right-censored, with a median survival of 35 months. Factors enhancing survival included a 1.0 mg estrogen dose, higher body weight, and elevated serum hemoglobin levels. In contrast, age, tumor size, cardiovascular disease history, and a composite index of stage and histology grade were associated with reduced survival odds in prostate cancer patients.

Conclusions Prostate cancer patients had a median survival of 35 months, with survival rates at 60 months (31.7%) and 75 months (23.2%). The 1.0 mg estrogen dose proved most effective among tested doses. The Weibull AFT model, with an AIC score of 3469.032, was the best-fit model.