Establishing a Competing Risk Regression Nomogram Model for Survival Data
October 23rd, 2020
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.
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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Overview
This protocol outlines the construction of nomograms using the Cox proportional hazards regression model and the competing risk regression model. The competing risk method is particularly useful when analyzing survival data with competing events.
Key Study Components
Area of Science
- Survival analysis
- Statistical modeling
- Competing risks
Background
- Competing events can influence survival outcomes.
- Traditional methods may not adequately address these competing risks.
- The Cox proportional hazards model is a standard approach in survival analysis.
- Nomograms provide a visual representation of risk predictions.
Purpose of Study
- To develop a protocol for creating nomograms based on survival analysis.
- To demonstrate the application of competing risk regression models.
- To enhance the evaluation of survival probabilities in the presence of competing events.
Methods Used
- Install and load the RMS and competing risk R packages.
- Import cohort data for analysis.
- Fit the Cox proportional hazards regression model using the CPH function.
- Develop a Cox regression nomogram for predicted survival rates.
Main Results
- Establishment of a Cox regression nomogram for two-year survival rates.
- Calculation of risk scores using the meta package in R.
- Division of the cohort into subgroups based on group risk scores.
- Visualization of results through forest plots.
Conclusions
- The competing risk regression model offers a more rational approach for survival analysis.
- Nomograms can effectively communicate risk predictions.
- This protocol can be applied to various studies involving competing risks.
Frequently Asked Questions
Executive Industry Relevance
In oncology and cardiovascular drug development, accurate survival analysis is critical for evaluating therapeutic efficacy and safety. Competing events such as treatment-related mortality or non-disease deaths can confound traditional Kaplan-Meier and Cox models, leading to biased risk estimates. This protocol enables biopharma R&D teams to apply competing risk regression models and nomograms to improve prognostic stratification, enhance mechanistic de-risking, and support go/no-go decisions in preclinical and early clinical development.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by distinguishing disease-specific mortality from competing risks, improving target confidence.
- Operational Value: Supports biological de-risking through refined risk stratification in preclinical models where non-target events may obscure treatment effects.
Screening & Assay Development
- Scientific Value: Facilitates preparation of validated biological systems for downstream workflows by providing quantitative, competing-risk-adjusted survival endpoints.
- Operational Value: Enhances assay standardization and reproducibility through nomogram-derived risk scores that enable consistent compound evaluation across studies.
Translational & Preclinical Research
- Scientific Value: Promotes translational continuity by aligning preclinical survival endpoints with clinical reality where competing events influence outcomes.
- Operational Value: Supports risk-adjusted advancement decisions by identifying subgroups (low/medium/high risk) based on group risk scores derived from competing risk models.
Pipeline & Workflow Integration
This method integrates into the discovery continuum from target validation through preclinical development, particularly when survival endpoints are confounded by non-informative censoring or competing risks.
- Discovery Biology: Supports hypothesis testing and pathway clarification by isolating the effect of genetic or pharmacological interventions on disease-specific outcomes.
- Screening: Enables assay readiness and reproducibility through forest plot visualization of hazard ratios and group risk scores across experimental conditions.
- Analytics: Delivers quantitative dependent variable measurements (cumulative incidence, hazard ratios, risk scores) that allow cross-functional teams to compare treatment effects objectively.
- Translational Research: Connects discovery to preclinical validation by ensuring survival analysis reflects real-world complexity where competing events are present.
- Enterprise Reuse: Establishes a reusable statistical framework for survival analysis across therapeutic areas, reducing redundant model development.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence by reducing mechanistic ambiguity in survival data through proper handling of competing risks.
- Operational Value: Improves standardization and scalability via R-based workflows (RMS, competing risk, meta packages) that can be deployed across teams.
- Strategic Value: Enhances go/no-go decision-making by providing risk-stratified subgroups, reducing late-stage failure due to unanticipated competing mortality.
- Portfolio Impact: Enables risk-adjusted prioritization of candidates based on competing risk-adjusted survival probabilities rather than crude event rates.
Implementation Considerations
- Requires expertise in survival analysis and R programming, including familiarity with CPH, CRR, and nomogram functions.
- Dependence on R environment with RMS, competing risk, and meta packages installed and properly configured.
- Necessitates cross-team standardization of variable selection (e.g., marital status, race, histological type, T/N classification) to ensure model comparability.
- Requires adaptation of prognostic variable matrices when applying the model to different disease models or preclinical systems.
- Limited by the assumption of proportional hazards; model diagnostics (calibration, discrimination) must be performed to validate performance.
Biopharma FAQs
Chapters in this video
0:04
Introduction
0:42
Nomogram Based on the Cox Proportional Hazards Regression Model
1:13
Subgroup Analysis Based on the Group Risk Score
1:43
Nomogram Based on the Competing Risk Regression Model
2:39
Results: Competing-risk Nomogram and Risk Score Estimation
4:10
Conclusion
