Abstract
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.
Highlights
In our previous study [1] [2], we obtained the parametric, non-parametric, and semi-parametric analysis of the survival times of 48 patients diagnosed with multiple myeloma
We developed the survival function from the nonlinear statistical model and use it to estimate the proportion of patients survival of MM cancer beyond a given survival time, and compared it with the survival function of the commonly used Cox Proportional Hazard (Cox-PH) model as a means of survival data analysis of the survival time as a function of covariates or risk factors
We generated t1*,t2*,t3*00 survival times that are based on the risk factors that have been identified for each patient of MM
Summary
In our previous study [1] [2], we obtained the parametric, non-parametric, and semi-parametric analysis of the survival times of 48 patients diagnosed with multiple myeloma. Most research studies use the Cox-PH model without satisfying the underlying assumptions and finding the interaction among covariates This makes it difficult to justify the genuineness of conclusions made from using the Cox-PH model and the accuracy of predicting the proportion of survival. 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. 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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