<strong>Background:</strong> 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 fa...<strong>Background:</strong> 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. <strong>Methods:</strong> 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. <strong>Results:</strong> 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. <strong>Conclusion:</strong> 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.展开更多
目的:探讨Cox比例风险模型与加速失效时间模型(accelerated failure time model,AFT)在基因表达数据生存分析中的应用及比较。方法:针对基因表达数据高维小样本的特性,首先采用偏最小二乘法对基因数据集进行降维,然后以降维后的成分为...目的:探讨Cox比例风险模型与加速失效时间模型(accelerated failure time model,AFT)在基因表达数据生存分析中的应用及比较。方法:针对基因表达数据高维小样本的特性,首先采用偏最小二乘法对基因数据集进行降维,然后以降维后的成分为协变量对两类生存模型进行拟合并比较其性能。结果:两类模型中病人生存时间的对数秩检验表明,不同风险组生存时间的差异均有统计学意义(P<0.01),而AFT模型比Cox模型具有更大的检验统计量的值。结论:Cox模型和AFT模型都适用于基因表达数据的生存分析,在某些实际应用中AFT模型的拟合效果可能更优于Cox模型。展开更多
文摘<strong>Background:</strong> 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. <strong>Methods:</strong> 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. <strong>Results:</strong> 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. <strong>Conclusion:</strong> 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.
文摘目的:探讨Cox比例风险模型与加速失效时间模型(accelerated failure time model,AFT)在基因表达数据生存分析中的应用及比较。方法:针对基因表达数据高维小样本的特性,首先采用偏最小二乘法对基因数据集进行降维,然后以降维后的成分为协变量对两类生存模型进行拟合并比较其性能。结果:两类模型中病人生存时间的对数秩检验表明,不同风险组生存时间的差异均有统计学意义(P<0.01),而AFT模型比Cox模型具有更大的检验统计量的值。结论:Cox模型和AFT模型都适用于基因表达数据的生存分析,在某些实际应用中AFT模型的拟合效果可能更优于Cox模型。
