A New Statistical Modeling Approach for Survival Analysis of Cancer Patients—Multiple Myeloma Cancer
A New Statistical Modeling Approach for Survival Analysis of Cancer Patients—Multiple Myeloma Cancer作者机构：Department of Mathematics and Statistics University of South Florida Tampa USA
出 版 物：《Open Journal of Applied Sciences》 (应用科学（英文）)
年 卷 期：2021年第10卷第4期
页 面：365-378页
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：Health Science Multiple Myeloma Cancer Cancer Therapeutic Cox-PH Model Statistical Model Survival Analysis Probability Estimation
摘 要：Background: 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. This is because the necessary assumptions in most cases are difficult to satisfy, as well as the assessment of interaction among covariates. Methods: To further improve the therapeutic/treatment strategy for cancer diseases, we proposed a new approach to survival analysis using multiple myeloma (MM) 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. Results: The 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. Also, satisfying the proposed model assumptions and finding interactions among risk factors is less difficult compared to the Cox-PH model. The proposed model can predict the real values of the survival times and the identified risk factors are ranked according to the percent of contribution to the survival time. Conclusion: The new proposed nonlinear statistical model approach for survival analysis of cancer diseases is very efficient and provides an improved and innovative strategy for cancer therapeutic/treatment.
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