title: Models for the analysis of repeated continuous outcome measures in clinical trials.
abstract: Repeated continuous outcome measures are common in clinical trials. In this tutorial style paper, using data collected from a trial evaluating an intervention for managing asthma and chronic obstructive pulmonary disease, we demonstrate ways of statistically analysing such data to answer frequently encountered clinical research questions. We illustrate the use of linear mixed effects modelling in doing so and discuss its advantages over several other commonly used approaches. The methods described in this paper can easily be carried out using standard statistical software.
authors.affiliation: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.|Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.
authors.forename: Alysha M
authors.initials: AM
authors.lastname: De Livera
authors.affiliation: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.|Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.
authors.forename: Sophie
authors.initials: S
authors.lastname: Zaloumis
authors.affiliation: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.|Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.
authors.forename: Julie A
authors.initials: JA
authors.lastname: Simpson
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012984:Software
keywords: hierarchical model; linear mixed effects model; longitudinal data; multilevel model; respiratory