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.引言：针对生存数据，决定系数无法用于表征模型对数据的拟合优度。为此，近年来学界已提出多种适用于生存数据的解释变异量测度。 方法：我们针对某一现有解释变异量测度的极小化性质展开分析，证实该测度并不满足该性质要求。 结果：借鉴线性回归分析中的最小二乘法，我们针对分类协变量开发了一种仅基于Kaplan-Meier估计量（Kaplan-Meier estimator）的新型解释变异量测度。该测度属于完全非参数化量度，具备简洁的图形化解释特性，且支持多种加权方案与显著性统计检验。最终，我们将该新型量度与其他现有解释变异量测度，应用于一组包含组织学乳头状甲状腺癌患者的数据集。 结论：我们提出了一种推导过程清晰易懂、具备图形化解释能力的新型生存数据解释变异量测度，可应用于后续的生存数据分析工作。