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

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JournalStatistics in Medicine
DateAccepted/In press - 13 Jun 2016
DateE-pub ahead of print - 11 Jul 2016
DatePublished (current) - 5 Oct 2016
Issue number26
Volume35
Number of pages15
Pages (from-to)4764-4778
Early online date11/07/16
Original languageEnglish

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 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.

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© 2016 John Wiley & Sons, Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. Embargo 12 months

    Research areas

  • model selection, multinomial logistic regression, penalized likelihood, risk prediction, varying coefficients

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