en
Scientific article
Meta-analysis
Open access
English

Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions

Published inStatistical methods in medical research, vol. 31, no. 7, p. 1355-1373
Publication date2022-04-26
First online date2022-04-26
Abstract

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models’ performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain.

eng
Keywords
  • Real-world effectiveness
  • Efficacy-effectiveness gap
  • Individual patient data
  • Network meta-analysis
  • Non-randomized studies
Funding
Citation (ISO format)
SEO, Michael et al. Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions. In: Statistical methods in medical research, 2022, vol. 31, n° 7, p. 1355–1373. doi: 10.1177/09622802221090759
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Article (Published version)
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Identifiers
ISSN of the journal0962-2802
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