Tools developed by the team

Live Network Meta-Analysis

For many conditions, multiple competing treatments are available, many of which have been assessed in randomized trials. Clinicians and patients who are making medical decisions need to know which treatments work best among all treatments available for the condition of interest. They increasingly use meta-analyses that synthesize the results of randomized trials to inform the relative efficacy and safety of the different treatments.
But conventional meta-analyses do not provide an exhaustive up-to-date synthesis of all available treatments, and thus prevent from answering easily to the real questions of interest.

We propose to switch:

  • from a series of conventional meta-analyses focusing on specific treatments (many treatments being not considered), performed at a given time and frequently out-of-date
  • to a single systematic review and evidence synthesis (with meta-analyses and network meta-analyses) covering all treatments and systematically updated when new trial results become available

We call this approach “live cumulative network meta-analysis”.

Live network meta-analysis

Power and sample size calculation for meta-epidemiological studies

Meta-epidemiological studies are used to compare treatment effect estimates between randomized clinical trials with and without a characteristic of interest. In this method, one identifies a number of meta-analyses that included at least one trial with and without the characteristic, concerning a variety of medical conditions and interventions. For each meta-analysis, treatment effect estimates are compared between trials with and without the characteristic (eg by estimating a ratio of odds ratios or a difference in standardized mean differences). The mean impact of the characteristic is then estimated across all meta-analyses.

Meta Epidemio