期刊
STATISTICS IN MEDICINE
卷 35, 期 26, 页码 4764-4778出版社
WILEY
DOI: 10.1002/sim.7034
关键词
model selection; multinomial logistic regression; penalized likelihood; risk prediction; varying coefficients
类别
资金
- UK Medical Research Council [MR/M025152/1]
- key international collaboration project - National Natural Science Foundation of China [71420107023]
- MRC [MR/M025152/1] Funding Source: UKRI
This paper proposes a risk prediction model using semi-varying coefficient multinomial logistic regression. We use a penalized local likelihood method to do the model selection and estimate both functional and constant coefficients in the selected model. The model can be used to improve predictive modelling when non-linear interactions between predictors are present. We conduct a simulation study to assess our method's performance, and the results show that the model selection procedure works well with small average numbers of wrong-selection or missing-selection. We illustrate the use of our method by applying it to classify the patients with early rheumatoid arthritis at baseline into different risk groups in future disease progression. We use a leave-one-out cross-validation method to assess its correct prediction rate and propose a recalibration framework to evaluate how reliable are the predicted risks. Copyright (C) 2016 John Wiley & Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据