Projects per year
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 complexity of the closed-form integration, we propose a general numerical approach using Quasi-Monte Carlo integration. Covariate correlation structures are accounted for using copulae. Crucially for decision making, comparisons may be provided in any target population with a given covariate distribution.
We illustrate the method with a network of plaque psoriasis treatments. Estimated population-average treatment effects are similar across study populations, as differences in the distributions of effect modifiers are small. Better fit is achieved than a random effects NMA, uncertainty is substantially reduced by explaining within- and between-study variation, and estimates are more interpretable.
|Number of pages||22|
|Journal||Journal of the Royal Statistical Society: Series A|
|Early online date||7 Jun 2020|
|Publication status||Published - 18 Jun 2020|
- effect modification
- indirect comparison
- individual patient data
- network meta-analysis
Supervisor: Welton, N. (Supervisor) & Dias, S. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)