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首页> 外文期刊>Journal of applied statistics >A model-based concordance-type index for evaluating the added predictive ability of novel risk factors and markers in the logistic regression models
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A model-based concordance-type index for evaluating the added predictive ability of novel risk factors and markers in the logistic regression models

机译：基于模型的一致性类型指数，用于评估逻辑回归模型中新型风险因素和标记物的附加预测能力

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摘要

The Concordance statistic (C-statistic) is commonly used to assess the predictive performance (discriminatory ability) of logistic regression model. Although there are several approaches for the C-statistic, their performance in quantifying the subsequent improvement in predictive accuracy due to inclusion of novel risk factors or biomarkers in the model has been extremely criticized in literature. This paper proposed a model-based concordance-type index, C-K, for use with logistic regression model. The C-K and its asymptotic sampling distribution is derived following Gonen and Heller's approach for Cox PH model for survival data but taking necessary modifications for use with binary data. Unlike the existing C-statistics for logistic model, it quantifies the concordance probability by taking the difference in the predicted risks between two subjects in a pair rather than ranking them and hence is able to quantify the equivalent incremental value from the new risk factor or marker. The simulation study revealed that the C-K performed well when the model parameters are correctly estimated for large sample and showed greater improvement in quantifying the additional predictive value from the new risk factor or marker than the existing C-statistics. Furthermore, the illustration using three datasets supports the findings from simulation study.
机译：一致性统计量（C-statistic）通常用于评估逻辑回归模型的预测性能（判别能力）。尽管C统计有多种方法，但由于在模型中包含了新的危险因素或生物标记物，因此在量化随后的预测准确性提高方面的表现受到了文献的强烈批评。本文提出了基于模型的一致性类型索引C-K，用于Logistic回归模型。 C-K及其渐近采样分布是根据Gonen和Heller的Cox PH模型（用于生存数据）得出的，但是对二进制数据进行了必要的修改。与现有的逻辑模型C统计不同，它通过采用成对的两个对象之间的预测风险差异来量化一致性概率，而不是对其进行排名，因此能够从新的风险因子或标记中量化等效增量值。仿真研究表明，当正确估计大型样本的模型参数时，C-K表现良好，并且与现有的C统计量相比，从新的风险因素或标记物量化附加预测值方面显示出更大的改进。此外，使用三个数据集的插图支持了仿真研究的结果。

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