在许多生物医学应用中,我们对数值结果高于阈值的预测概率比对结果的预测值更感兴趣.例如,已知抗体水平高于某一阈值可提供对疾病的免疫力,或疾病严重程度评分的阈值可能反映了从症状前疾病阶段到有症状疾病阶段的转变。因此,生物医学研究人员通常将数值转换为二元结果(信息丢失)以进行逻辑回归(概率解释)。我们通过用逻辑回归对二元结果进行建模来解决这种糟糕的统计实践,用线性回归对数值结果进行建模,将预测值从线性回归转换为预测概率,并结合逻辑回归和线性回归的预测概率。分析高维模拟和实验数据,即预测认知障碍的临床数据,我们获得了显著改善的二分法结局预测.因此,所提出的方法有效地将二进制与数值结果相结合,以改善高维设置中的二进制分类。GitHub(https://github.com/rauschenberger/cornet)和CRAN(https://CRAN)上的R包cornet中提供了一个实现。R-project.org/package=cornet)。
In many biomedical applications, we are more interested in the predicted probability that a numerical outcome is above a threshold than in the predicted value of the outcome. For example, it might be known that antibody levels above a certain threshold provide immunity against a disease, or a threshold for a disease severity score might reflect conversion from the presymptomatic to the symptomatic disease stage. Accordingly, biomedical researchers often convert numerical to binary outcomes (loss of information) to conduct logistic regression (probabilistic interpretation). We address this bad statistical practice by modelling the binary outcome with logistic regression, modelling the numerical outcome with linear regression, transforming the predicted values from linear regression to predicted probabilities, and combining the predicted probabilities from logistic and linear regression. Analysing high-dimensional simulated and experimental data, namely clinical data for predicting cognitive impairment, we obtain significantly improved predictions of dichotomised outcomes. Thus, the proposed approach effectively combines binary with numerical outcomes to improve binary classification in high-dimensional settings. An implementation is available in the R package cornet on GitHub (https://github.com/rauschenberger/cornet) and CRAN (https://CRAN.R-project.org/package=cornet).