Multilevel network meta-regression for population-adjusted treatment comparisons.

ABSTRACT: Standard network meta-analysis (NMA) and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any effect modifiers are balanced across populations. Population adjustment methods relax this assumption using individual patient data from one or more studies. However, current matching-adjusted indirect comparison and simulated treatment comparison methods are limited to pairwise indirect comparisons and cannot predict into a specified target population. Existing meta-regression approaches incur aggregation bias. We propose a new method extending the standard NMA framework. An individual level regression model is defined, and aggregate data are fitted by integrating over the covariate distribution to form the likelihood. Motivated by the com

SUBMITTER: Phillippo DM 

PROVIDER: S-EPMC7362893 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature