A New Statistical Modeling Approach for Survival Analysis of Cancer Patients: Multiple Myeloma Cancer

• Activity Number: 90 - Novel Statistical Methods for COVID Pandemic and Other Current Health Policy Issues
• Type: Contributed
• Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Health Policy Statistics Section
• Author(s): Lohuwa Mamudu* and Chris P. Tsokos
• Companies: University of South Florida and University of South Florida
• Keywords: Health Science; Cancer Therapeutic; Nonlinear Statistical Model; Cancer Survival Analysis; Cox-PH Model; Probability Estimation

Abstract

The Cox Proportional Hazard (Cox-PH) model has been a popularly used method for survival analysis of cancer data given the survival times as a function of covariates or risk factors. However, it is very seldom to see the assumptions for the application of the Cox-PH model satisfied in most of the research studies, raising questions about the effectiveness, robustness, and accuracy of the model predicting the proportion of survival times. To further improve the therapeutic/treatment strategy for cancer diseases, we proposed a new approach to survival analysis using multiple myeloma cancer data. We first developed a data-driven nonlinear statistical model that predicts the survival times with 93% accuracy. We then performed a parametric analysis on the predicted survival times to obtain the survival function which is used in estimating the proportion of survival times. This new proposed approach for survival analysis has proved to be more robust and gives better estimates of the proportion of survival than the Cox-PH model, and satisfying assumptions and finding interactions is less difficult than Cox-PH. Our new approach enhances the treatment strategy of cancer survivorship.