Data from: A novel nonparametric measure of explained variation for survival data with an easy graphical interpretation

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.