Cancer Research
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为生存数据建立竞争风险回归 Nomogram 模型
Summary October 23rd, 2020
Please note that all translations are automatically generated.
Click here for the English version.
此处介绍的是基于考克斯比例危险回归模型和竞争风险回归模型构建符号图的协议。竞争方法是在生存分析中存在竞争事件时应用的一种更为合理的方法。
Transcript
This is the protocol to deal with the normal grants based on the competing risk regression model which is a more rational method to apply when the competing events such as the traffic accidents are passed in the survival analysis. Individuals who experienced our competing events often are reminding the risks that, as the competing risks are usually not independent. The competing normal grant is more appropriate to evaluate the probability of testing events for individuals in analysis.
After installing the RMS and competing risk R packages, load them and import the cohort data To establish the Cox proportional hazards regression model, fit the model to the data using the CPH function then develop a Cox regression nomogram, taking the two year predicted survival rate as an example. Use the meta package in R to calculate the risk score and draw a forest plot. After installing and loading the R packages, obtain the group risk score, or GRS and divide the cohort into three subgroups.
Then draw the forest plot, obtaining the hazard ratio lower competence interval, and upper confidence interval with the CRR function. To establish the competing risk regression model, start by placing prognostic variables into a matrix. Use the Cbind function to concatenate the variables by columns and fit them into the model.
Then use the nomogram function to construct Cox Nome. Get the baseline cumulative incidents function, or CIF, and replace the X beta and X point of the competing risk regression model. Replace the total X point in X reel, then calculate the X score and plot the nomogram.
The equation for the X score and X rail relationship can be calculated according to the intrinsic attribution of the competing model. CIF zero means baseline CIF, which is calculated by the predict CRR function. In the example cohort a total of 8, 550 eligible patients were included in the analysis and the median followup time was 88 months.
The cumulative incidences of tumor death and no tumor death and competing events were by the Kaplan-Meier method, and the competing risk regression function, respectively. The sum of the cumulative incidences of tumor death and no tumor death, calculated with the Kaplan-Meier method, was higher than the sum of the estimates of all causes of death, which was equal to the cumulative incidents of cancer specific death when a competing method was used. A nomogram was constructed using the Cox proportional regression model based on significant factors, which included marital status, race, histological type, differentiated grade, T-classification and N-classification.
A nomogram was also constructed using the competing risk regression model. Based on the risk score, the cohort was classified into three subgroups:low risk, medium risk and high risk. The forest plot was used to clearly present the interaction between the group risk score and the specific factor.
When considering age, only the low risk group showed a worse prognosis for younger women, indicating that young age may act as a protective factor of prognosis in medium and high-risk groups. When attempting this protocol it is important to fully understand the different survive models in the 10 to event analysis and the tools are properly mastered for individualized guidance. The model performance is evaluated in terms of the discrimination and the caliber ration performance.
Following this procedure, the calibration curve and they're the same gaps can be performance to validate the efficiency of the competing normal grant.
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