Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning

Arnold, K. F., Davies, V. , de Kamps, M., Tennant, P. W.G., Mbotwa, J. and Gilthorpe, M. S. (2020) Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning. International Journal of Epidemiology, 49(6), pp. 2074-2082. (doi: 10.1093/ije/dyaa049) (PMID:32380551) (PMCID:PMC7825942)

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Abstract

Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice. We use the framework of generalized linear models (GLMs) to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation of results. In particular, we identify five primary ways in which GLMs for prediction differ from GLMs for causal inference: (i) the covariates that should be considered for inclusion in (and possibly exclusion from) the model; (ii) how a suitable set of covariates to include in the model is determined; (iii) which covariates are ultimately selected and what functional form (i.e. parameterization) they take; (iv) how the model is evaluated; and (v) how the model is interpreted. We outline some of the potential consequences of failing to acknowledge and respect these differences, and additionally consider the implications for machine learning (ML) methods. We then conclude with three recommendations that we hope will help ensure that both prediction and causal modelling are used appropriately and to greatest effect in health research.

Item Type:Articles
Additional Information:This work was supported by the Economic and Social Research Council [grant number ES/J500215/1 to K.F.A.]; The Alan Turing Institute (grant number EP/N510129/1 to P.W.G.T. and M.S.G.); and the Commonwealth Scholarship Commission (to J.M.).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Davies, Dr Vinny
Authors: Arnold, K. F., Davies, V., de Kamps, M., Tennant, P. W.G., Mbotwa, J., and Gilthorpe, M. S.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:International Journal of Epidemiology
Publisher:Oxford University Press
ISSN:0300-5771
ISSN (Online):1464-3685
Published Online:07 May 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in International Journal of Epidemiology 49(6): 2074-2082
Publisher Policy:Reproduced under a Creative Commons License

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