Name: establishing a competing risk regression nomogram model for survival data
Uploaded: 2020-10-23T13:00:00
Description: Watch this Scientific Journal Video about Establishing a Competing Risk Regression Nomogram Model for Survival Data at JoVE.com

The study was supported by grants from the general program of Zhejiang Province Natural Science Foundation (grant number LY19H160020) and key program of the Jinhua Municipal Science &#38; Technology Bureau (grant number 2016-3-005, 2018-3-001d and 2019-3-013).

The research protocol was approved by the Ethics Committee of Jinhua Hospital, Zhejiang University School of Medicine. For this experiment, the cases were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. SEER is an open-access database that includes demographic, incidence and survival data from 18 population-based cancer registries. We registered on the SEER website and signed a letter of assurance to acquire the research data (12296-Nov2018).
1. Data source
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The overall goal of the current study was to establish a specific competing-risk nomogram that could describe real-world diseases and to develop a convenient individual assessment model for clinicians to approach treatment decisions. Here, we provide a step-by-step tutorial for establishing nomograms based on the Cox regression model and competing risk regression model and further performing subgroup analysis. Zhang et al.18 introduced an approach to create a competing-risk nomogram, but the main ...

The time to event survival analysis is quite common in clinical studies. Survival data measure the time span from the start time until the occurrence of the event of interest, but the occurrence of the event of interest is often precluded by another event. If more than one type of end point is present, they are called competing risks end points. In this case, the standard hazard analysis (i.e., Cox proportional cause-specific hazards model) often does not work well because individuals experiencing another type of event are censored. Individuals who experience a competing event often remain in the risk set, as the competing risks are usually not independent. Therefore, Fine and Gray1 studied the regression model estimation for the sub distribution of a competing risk. In a competing risk setting, three different types of events can be discriminated.
One measures overall survival (OS) by demonstrating a direct clinical benefit from new treatment methods for a disease. OS measures the survival time from time of origin (i.e., time of diagnosis or treatment) to the time of death due to any cause and generally evaluates the absolute risk of death, thereby failing to differentiate the causes of death (e.g., cancer-specific death (CSD) or non-cancer-specific death (non-CSD))2. OS is, therefore, considered as the most important endpoint. The events of interest are often cancer related, while the non-cancer-specific events, which include heart disease, traffic accidents or other unrelated causes, are considered competing events. Malignant patients with a favorable prognosis, who are expected to survive longer, are often at a greater risk of non-CSD. That is, the OS will be diluted by other causes of death and fail to correctly interpret the real effectiveness of clinical treatment. Therefore, OS may not be the optimal measure for accessing the outcomes of disease3. Such biases could be corrected by the competing risk regression model.
There are two main methods for competing risk data: cause-specific hazard models (Cox models) and subdistribution hazard models (competing models). In the following protocol, we present two methods to generate nomograms based on the cause-specific hazard model and the subdistribution hazard model. The cause-specific hazard model can be made to fit in the Cox proportional hazards model, which treats subjects who experience the competing event as censored at the time that the competing event occurred. In the subdistribution hazard model that was introduced by Fine and Gray1 in 1999, three different types of events can be discriminated, and individuals who experience a competing event remain at the risk set forever.
A nomogram is a mathematical representation of the relationship between three or more variables4. Medical nomograms consider biological and clinical event as variables (e.g., tumor grade and patient age) and generate probabilities of a clinical event (e.g., cancer recurrence or death) that is graphically depicted as a statistical prognostic model for a given individual. Generally, a nomogram is formulated based on the results of the Cox proportional hazards model5,6,7,8,9,10.
However, when competing risks are present, a nomogram based on the Cox model might fail to perform well. Though several previous studies11,12,13,14 have applied the competing risk nomogram to estimate the probability of CSD, few studies have described how to establish the nomogram based on a competing risk regression model, and there is no existing package available to accomplish this. Therefore, the method presented below will provide a step-by-step protocol to establish a specific competing-risk nomogram based on a competing risk regression model as well as a risk score estimation to aid clinicians in treatment decision-making.

establishing a competing risk regression nomogram model for survival data

The Kaplan&#8211;Meier method and Cox proportional hazards regression model are the most common analyses in the survival framework. These are relatively easy to apply and interpret and can be depicted visually. However, when competing events (e.g., cardiovascular and cerebrovascular accidents, treatment-related deaths, traffic accidents) are present, the standard survival methods should be applied with caution, and real-world data cannot be correctly interpreted. It may be desirable to distinguish different kinds of events that may lead to the failure and treat them differently in the analysis. Here, the methods focus on using the competing regression model to identify significant prognostic factors or risk factors when competing events are present. Additionally, nomograms based on a proportional hazard regression model and a competing regression model are established to help clinicians make individual assessments and risk stratifications in order to explain the impact of controversial factors on prognosis.

Survival characteristics of the example cohort 
In the example cohort, a total of 8,550 eligible patients were included in the analysis and the median follow-up time was 88 months (range, 1 to 95 months). A total of 679 (7.94%) patients were younger than 40 years old and 7,871 (92.06%) patients were older than 40. At the end of the trial, 7,483 (87.52%) patients were still alive, 662 (7.74%) died because of breast cancer, and 405 (4.74%) patients died because of other causes (competing risks).
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Watch this Scientific Journal Video about Establishing a Competing Risk Regression Nomogram Model for Survival Data at JoVE.com

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Presented here is a protocol to build nomograms based on the Cox proportional hazards regression model and competing risk regression model. The competing method is a more rational method to apply when competing events are present in the survival analysis.

The Kaplan&#8211;Meier method and Cox proportional hazards regression model are the most common analyses in the survival framework. These are relatively ...

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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