A plethora of statistical methods and algorithms have become more widely accessible over recent years, sometimes making it challenging to determine which methods to utilize. Each method has advantages and disadvantages, and different methods may be better suited for different types of data. We investigated the difference in performance among various modeling techniques for survival analysis, including proportional hazards regression, conditional inference forests, and random forest for survival outcomes. We then applied these methods to a database of patients diagnosed with early stage oral squamous cell carcinoma and compared the performance of these methods using integrated Brier scores. The results of the analysis showed that all models similarly identified the driving factors associated with recurrence of oral cancer. Conditional inference forests performed better with regards to integrated Brier score, indicating better performance in predictive ability.