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

TL;DR

This paper introduces computationally efficient methods, weighted regression and meta-analysis, for fitting mixed models to large electronic health records data, enabling faster analysis while maintaining accuracy.

Contribution

The paper presents two novel methods, weighted regression and meta-analysis, for analyzing tall electronic health records data with mixed models, improving computational efficiency.

Findings

Both methods produce similar point estimates to full data analysis.

Weighted regression is equivalent to full data fitting for discrete data.

Meta-analysis is effective with continuous covariates.

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.