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Semi-varying coefficient multinomial logistic regression for disease progression risk prediction

Ke, Y; Fu, B; Zhang, W; (2016) Semi-varying coefficient multinomial logistic regression for disease progression risk prediction. Statistics in Medicine , 35 (26) pp. 4764-4778. 10.1002/sim.7034. Green open access

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Abstract

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 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.

Type: Article
Title: Semi-varying coefficient multinomial logistic regression for disease progression risk prediction
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/sim.7034
Publisher version: http://dx.doi.org/10.1002/sim.7034
Language: English
Additional information: This is the peer reviewed version of the following article: Ke, Y; Fu, B; Zhang, W; (2016) Semi-varying coefficient multinomial logistic regression for disease progression risk prediction. Statistics in Medicine , 35 (26) pp. 4764-4778, which has been published in final form at http://dx.doi.org/10.1002/sim.7034. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving
Keywords: Model selection;multinomial logistic regression; penalized likelihood; risk prediction; varying coefficients
UCL classification: UCL
URI: https://discovery.ucl.ac.uk/id/eprint/1493623
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