Penalised regression splines: theory and application to medical research


Marra, G; Radice, R; (2010) Penalised regression splines: theory and application to medical research. Statistical methods in medical research, 19 (2). pp. 107-125. ISSN 0962-2802 DOI: https://doi.org/10.1177/0962280208096688

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Abstract

Generalised additive models (GAMs) allow for flexible functional dependence of a response variable on covariates. The aim of this article is to provide an accessible overview of GAMs based on the penalised likelihood approach with regression splines. In contrast to the classical backfitting, the penalised likelihood framework taken here provides researchers with an efficient computational method for automatic multiple smoothing parameter selection, which can determine the functional form of any relationship from the data. We illustrate through an example how the use of this methodology can help to gain insights into medical research.

Item Type: Article
Keywords: Biomedical Research, statistics & numerical data, Models, Statistical, Regression Analysis
Faculty and Department: Faculty of Public Health and Policy > Dept of Health Services Research and Policy
PubMed ID: 18815162
Web of Science ID: 277010900001
URI: http://researchonline.lshtm.ac.uk/id/eprint/1694

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