AI 中文总结
针对受控条件下逻辑回归模型数据生成的难题，提出集成迭代二分算法，可确定截距与回归系数以生成指定患病率和预测准确率的数据，经模拟验证性能，且需考虑参数选择与预测准确率的关系用于模型评估数据生成。
AI 中文摘要
在受控条件下生成具有所需特性的逻辑回归模型数据颇具挑战性。我们提出一种集成迭代二分算法，该算法可确定截距与回归系数的值，从而生成具有指定患病率和预测准确率的数据。通过模拟研究验证了所提方法的性能。我们进一步表明，参数值的选择，或在贝叶斯设定下这些参数先验的选择，会诱导出特定水平的预测准确率。因此，在受控条件下生成用于模型评估的数据时，应考虑这种关系。
英文摘要
Generating data with desired properties for a logistic regression model under controlled conditions is challenging. We propose an integrated iterative bisection algorithm that determines values of the intercept and regression coefficients capable of generating data with a specified prevalence and predictive accuracy. The performance of the proposed method is demonstrated through simulation studies. We further show that the choice of parameter values, or, in a Bayesian setting, the choice of priors for these parameters, induces a particular level of predictive accuracy. This relationship should therefore be taken into account when generating data for model evaluation under controlled conditions.
Comments15 pages, 6 figures
