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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Statistical primer: using prognostic models to predict the future: what cardiothoracic surgery can learn from
Jamie A Mawhinney1, Craig A Mounsey2, Alastair O'Brien3
1Pulvertaft Hand Centre, Royal Derby Hospital, Derby, UK.
Insights
Prognostic models in medicine, like those in cardiothoracic surgery, can be misapplied. Using a TV dancing show example, this study highlights issues with risk scores, emphasizing cautious interpretation of demographic data alone.
Area of Science:
- Medical Prognostication
- Statistical Modeling
- Clinical Decision Support
Background:
- Prognostic models are integral to medical assessment, particularly in cardiothoracic surgery, with tools like EuroSCORE being widely adopted.
- Despite their utility, these predictive models face challenges and potential misapplication in clinical practice.
- Understanding the limitations of prognostic models is crucial for accurate patient assessment and treatment planning.
Purpose of the Study:
- To illustrate common issues and pitfalls in the application and interpretation of prognostic models.
- To use a non-medical example, Strictly Come Dancing, to demonstrate the complexities of predictive modeling.
- To emphasize the need for caution when utilizing prognostic models in cardiothoracic surgery and other medical fields.
Main Methods:
- A multivariable prognostic model was developed using data from 19 series of Strictly Come Dancing.
- The model aimed to prospectively predict the outcomes of the 20th series of the show.
- Model performance was evaluated using R-squared and Spearman's rank correlation coefficients.
Main Results:
- An initial model based on demographic data alone showed limited predictive value (R2=0.25, Spearman's=0.22).
- Incorporating early judges' scores significantly improved the model's predictive power (R2=0.40, Spearman's=0.30).
- The findings underscore the importance of including relevant performance metrics beyond basic demographics.
Conclusions:
- Prognostic models require careful and judicious use by researchers and clinicians.
- Extrapolating conclusions solely from demographic data in prognostic models can lead to inaccuracies.
- Models must adequately capture essential prognostic information to be reliable in clinical decision-making.
Objectives:
Prognostic models are widely used across medicine and within cardiothoracic surgery, where predictive tools such as EuroSCORE are commonplace. Such models are a useful component of clinical assessment but may be misapplied. In this article, we demonstrate some of the major issues with risk scores by using the popular BBC television programme Strictly Come Dancing (known as Dancing with the Stars in many other countries) as an example.
Methods:
We generated a multivariable prognostic model using data from the then-completed 19 series of Strictly Come Dancing to predict prospectively the results of the 20th series.
Results:
The initial model based solely on demographic data was limited in its predictive value (0.25, 0.22; R2 and Spearman's rank correlation, respectively) but was substantially improved following the introduction of early judges' scores deemed representative of whether contestants could actually dance (0.40, 0.30). We then utilize our model to discuss the difficulties and pitfalls in using and interpreting prognostic models in cardiothoracic surgery and beyond, particularly where these do not adequately capture potentially important prognostic information.
Conclusion:
Researchers and clinicians alike should use prognostic models cautiously and not extrapolate conclusions from demographic data alone.
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