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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Validity of a nomogram to predict long distance running performance.
Jérémy B J Coquart1, Morgan Alberty, Laurent Bosquet
1Laboratory of Human Movement Studies, Faculty of Sports Sciences and Physical Education, Lille 2 University, Ronchin, France. jeremy.coquart@voila.fr
Journal of Strength and Conditioning Research
|October 27, 2009
Summary
This study validates a nomogram for predicting running performance across 10 km, 20 km, and marathon distances. The nomogram shows good accuracy, supporting its use in training and race strategy.
Area of Science:
- Sports Science
- Exercise Physiology
- Performance Analytics
Background:
- Accurate prediction of running performance is crucial for training prescription and race strategy.
- Existing predictive tools require validation across various distances.
Purpose of the Study:
- To assess the validity of a nomogram for predicting running performance.
- To evaluate prediction accuracy for 10 km, 20 km, and marathon distances.
Main Methods:
- Utilized official French Athletics Federation rankings (2002-2006) for 330 male runners competing in multiple distances.
- Compared actual performances against nomogram-predicted performances using Wilcoxon signed-rank tests, effect sizes, correlation, and Bland-Altman plots.
Main Results:
- The nomogram demonstrated validity across 10 km, 20 km, and marathon distances.
- Overestimation at 10 km (13s) and underestimation at 20 km (27s) were statistically significant but with trivial effect sizes.
- High correlations (0.89-0.97) and acceptable limits of agreement (6.1-13.2%) indicate good predictive accuracy, especially for interpolated performances.
Conclusions:
- The nomogram is a valid tool for predicting running performance at 10 km, 20 km, and marathon.
- Interpolated predictions offer enhanced accuracy, suitable for setting training intensities and race strategies.
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