Some consequences of assuming simple patterns for the treatment effect over time in a linear mixed model


Bamia, C; White, IR; Kenward, MG; (2013) Some consequences of assuming simple patterns for the treatment effect over time in a linear mixed model. Statistics in medicine, 32 (15). pp. 2585-2594. ISSN 0277-6715 DOI: https://doi.org/10.1002/sim.5707

Full text not available from this repository. (Request a copy)

Abstract

Linear mixed models are often used for the analysis of data from clinical trials with repeated quantitative outcomes. This paper considers linear mixed models where a particular form is assumed for the treatment effect, in particular constant over time or proportional to time. For simplicity, we assume no baseline covariates and complete post-baseline measures, and we model arbitrary mean responses for the control group at each time. For the variance-covariance matrix, we consider an unstructured model, a random intercepts model and a random intercepts and slopes model. We show that the treatment effect estimator can be expressed as a weighted average of the observed time-specific treatment effects, with weights depending on the covariance structure and the magnitude of the estimated variance components. For an assumed constant treatment effect, under the random intercepts model, all weights are equal, but in the random intercepts and slopes and the unstructured models, we show that some weights can be negative: thus, the estimated treatment effect can be negative, even if all time-specific treatment effects are positive. Our results suggest that particular models for the treatment effect combined with particular covariance structures may result in estimated treatment effects of unexpected magnitude and/or direction. Methods are illustrated using a Parkinson's disease trial. Copyright (c) 2012 John Wiley & Sons, Ltd.

Item Type: Article
Keywords: repeated measures, random intercepts, random intercepts and slopes, covariance structure, clinical-trials, longitudinal data, statistics
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Research Centre: Centre for Statistical Methodology
PubMed ID: 23242852
Web of Science ID: 320181200006
URI: http://researchonline.lshtm.ac.uk/id/eprint/1105188

Statistics


Download activity - last 12 months
Downloads since deposit
0Downloads
336Hits
Accesses by country - last 12 months
Accesses by referrer - last 12 months
Impact and interest
Additional statistics for this record are available via IRStats2

Actions (login required)

Edit Item Edit Item