title: A model-based evaluation of single metrics for discriminating changes in rheumatoid arthritis disease activity.
abstract: Composite indices for quantifying rheumatoid arthritis (RA) disease activity such as the 28-joint disease activity score (DAS28) are comprised of single parameters ('metrics') in various combinations. Population modelling methods were used to evaluate single metrics for their ability to reflect changes in disease activity with a view to understanding and improving composite indices.
  
  
  
  A total of 11 single metrics of RA disease activity (tender and swollen joint counts, acute phase reactants and global health, pain and physical function assessments) were obtained from 203 patients with recent onset RA. Participants received combination disease-modifying anti-rheumatic drugs (DMARDs) according to a treat-to-target approach with a pre-defined protocol for treatment intensification. Models describing each metric's magnitude and variability of change from baseline to a single 'treated' state in the population were developed using nonmem(®) . Measures that displayed uniformly large changes between states across the population were ranked higher in terms of discriminatory capacity.
  
  
  
  Joint counts demonstrated a greater ability to discriminate changes in RA disease activity than others. Correlations between metrics demonstrated that erythrocyte sedimentation rate (ESR) had limited relationships with others for baseline scores and changes in RA disease activity (r generally < 0.2). However it appeared to be important in describing changes for those individuals where ESR levels were initially elevated.
  
  
  
  It appears unlikely that a single group of metrics may be suitable to capture disease activity changes across all RA patients and defining the most appropriate metric(s) for individual patients will be an important area of future research.
authors.affiliation: Australian Centre for Pharmacometrics, University of South Australia, Adelaide.|Sansom Institute for Health Research, University of South Australia, Adelaide.|School of Pharmacy and Medical Sciences, University of South Australia, Adelaide.
authors.forename: Jessica
authors.initials: J
authors.lastname: Wojciechowski
authors.affiliation: Sansom Institute for Health Research, University of South Australia, Adelaide.|School of Pharmacy and Medical Sciences, University of South Australia, Adelaide.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Wiese
authors.affiliation: Rheumatology Unit, Royal Adelaide Hospital, Adelaide.|Discipline of Medicine, University of Adelaide, Adelaide, Australia.
authors.forename: Susanna M
authors.initials: SM
authors.lastname: Proudman
authors.affiliation: Australian Centre for Pharmacometrics, University of South Australia, Adelaide.|Sansom Institute for Health Research, University of South Australia, Adelaide.|School of Pharmacy and Medical Sciences, University of South Australia, Adelaide.
authors.forename: David J R
authors.initials: DJ
authors.lastname: Foster
authors.affiliation: Australian Centre for Pharmacometrics, University of South Australia, Adelaide.|Sansom Institute for Health Research, University of South Australia, Adelaide.|School of Pharmacy and Medical Sciences, University of South Australia, Adelaide.
authors.forename: Richard N
authors.initials: RN
authors.lastname: Upton
mesh_terms: D000328:Adult; D000368:Aged; D018501:Antirheumatic Agents / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D012720:Severity of Illness Index
chemical_list: D018501:Antirheumatic Agents
keywords: disease activity; disease modifying anti-rheumatic drugs; population modelling; rheumatoid arthritis
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