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Computationally efficient methods for fitting mixed models to electronic health records data

Rhodes, KM; Turner, RM; Payne, RA; White, IR; (2018) Computationally efficient methods for fitting mixed models to electronic health records data. Statistics in Medicine , 37 (29) pp. 4557-4570. 10.1002/sim.7944. Green open access

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Abstract

Motivated by two case studies using primary care records from the Clinical Practice Research Datalink, we describe statistical methods that facilitate the analysis of tall data, with very large numbers of observations. Our focus is on investigating the association between patient characteristics and an outcome of interest, while allowing for variation among general practices. We explore ways to fit mixed-effects models to tall data, including predictors of interest and confounding factors as covariates, and including random intercepts to allow for heterogeneity in outcome among practices. We introduce (1) weighted regression and (2) meta-analysis of estimated regression coefficients from each practice. Both methods reduce the size of the dataset, thus decreasing the time required for statistical analysis. We compare the methods to an existing subsampling approach. All methods give similar point estimates, and weighted regression and meta-analysis give similar standard errors for point estimates to analysis of the entire dataset, but the subsampling method gives larger standard errors. Where all data are discrete, weighted regression is equivalent to fitting the mixed model to the entire dataset. In the presence of a continuous covariate, meta-analysis is useful. Both methods are easy to implement in standard statistical software.

Type: Article
Title: Computationally efficient methods for fitting mixed models to electronic health records data
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/sim.7944
Publisher version: http://dx.doi.org/10.1002/sim.7944
Language: English
Additional information: Copyright © 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: health records, meta-analysis, mixed-effects regression model, subsampling, tall data
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10055732
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