Hin L Y, Lau T K, Rogers M S, Chang A M
Department of Obstetrics and Gynaecology, Prince of Wales Hospital, Chinese University of Hong Kong, Shatin.
Stat Med. 1999 May 15;18(9):1101-10. doi: 10.1002/(sici)1097-0258(19990515)18:9<1101::aid-sim99>3.0.co;2-q.
In prediction model development, continuous variables are often dichotomized at empirically chosen thresholds to simplify calculations and facilitate decision making. However, these choices are often made in the absence of estimated covariate effects and may be inaccurate, thus weakening the models. To improve this approach, generalized additive modelling that allows non-parametric estimation of the true covariate effects is used for threshold selection. In this study, this approach is illustrated by development of prediction models for intrapartum Caesarean deliveries. The prediction performance of the models thus developed is significantly better than that developed using empirically chosen thresholds for dichotomization.
在预测模型开发中，连续变量常常在根据经验选择的阈值处进行二分，以简化计算并便于决策。然而，这些选择往往是在未估计协变量效应的情况下做出的，可能不准确，从而削弱了模型。为改进这种方法，采用允许对真实协变量效应进行非参数估计的广义相加模型来选择阈值。在本研究中，通过开发产时剖宫产的预测模型来说明这种方法。如此开发的模型的预测性能明显优于使用根据经验选择的二分阈值所开发的模型。
