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Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis.

David M PhillippoSofia DiasA E AdesMark BelgerAlan BrnabicDaniel SaureYves SchymuraNicky J Welton
Published in: Medical decision making : an international journal of the Society for Medical Decision Making (2022)
Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes.We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn.Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies.
Keyphrases
  • systematic review
  • magnetic resonance
  • high resolution
  • decision making
  • coronary artery disease
  • solid state
  • case report
  • metabolic syndrome
  • skeletal muscle
  • meta analyses
  • sensitive detection
  • replacement therapy