Abstract
SummaryStandard 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 complexity of the closed form integration, we propose a general numerical approach using quasi‐Monte‐Carlo integration. Covariate correlation structures are accounted for by using copulas. 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. A 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.
Highlights
Health policy and reimbursement decisions require unbiased causal estimates of the relative effectiveness of all relevant treatments or interventions for a given patient population
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
Summary
Health policy and reimbursement decisions require unbiased causal estimates of the relative effectiveness of all relevant treatments or interventions for a given patient population. The gold standard of evidence on relative effectiveness is randomized controlled trials in the population of interest. It is rare that all relevant treatments have been compared head to head in a single randomized controlled trial; instead, the evidence base is often comprised of several studies, each comparing a subset of the treatments of interest. NMA combines direct evidence (from head-to-head studies) and indirect evidence (via a common comparator) in a coherent manner from studies that form a connected network of treatment comparisons.
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More From: Journal of the Royal Statistical Society Series A: Statistics in Society
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