AI Article Synopsis
- The traditional coefficient of determination is inadequate for assessing the fit of survival data models, prompting the development of alternative measures of explained variation.
- A critical analysis of an existing measure reveals it does not meet minimization requirements, leading to the creation of a new nonparametric measure based on the Kaplan-Meier estimator.
- This novel measure allows for different weightings, statistical significance testing, and is demonstrated using data from patients with papillary thyroid carcinoma, offering a clear and understandable approach for future analyses in survival studies.
Article Abstract
Introduction: For survival data the coefficient of determination cannot be used to describe how good a model fits to the data. Therefore, several measures of explained variation for survival data have been proposed in recent years.
Methods: We analyse an existing measure of explained variation with regard to minimisation aspects and demonstrate that these are not fulfilled for the measure.
Results: In analogy to the least squares method from linear regression analysis we develop a novel measure for categorical covariates which is based only on the Kaplan-Meier estimator. Hence, the novel measure is a completely nonparametric measure with an easy graphical interpretation. For the novel measure different weighting possibilities are available and a statistical test of significance can be performed. Eventually, we apply the novel measure and further measures of explained variation to a dataset comprising persons with a histopathological papillary thyroid carcinoma.
Conclusion: We propose a novel measure of explained variation with a comprehensible derivation as well as a graphical interpretation, which may be used in further analyses with survival data.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4633600 | PMC |
| http://dx.doi.org/10.3205/000222 | DOI Listing |
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