Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis
arxiv(2024)
Abstract
Network meta-analysis combines aggregate data (AgD) from multiple randomised
controlled trials, assuming that any effect modifiers are balanced across
populations. Individual patient data (IPD) meta-regression is the "gold
standard" method to relax this assumption, however IPD are frequently only
available in a subset of studies. Multilevel network meta-regression (ML-NMR)
extends IPD meta-regression to incorporate AgD studies whilst avoiding
aggregation bias, but currently requires the aggregate-level likelihood to have
a known closed form. Notably, this prevents application to time-to-event
outcomes.
We extend ML-NMR to individual-level likelihoods of any form, by integrating
the individual-level likelihood function over the AgD covariate distributions
to obtain the respective marginal likelihood contributions. We illustrate with
two examples of time-to-event outcomes, showing the performance of ML-NMR in a
simulated comparison with little loss of precision from a full IPD analysis,
and demonstrating flexible modelling of baseline hazards using cubic M-splines
with synthetic data on newly diagnosed multiple myeloma.
ML-NMR is a general method for synthesising individual and aggregate level
data in networks of all sizes. Extension to general likelihoods, including for
survival outcomes, greatly increases the applicability of the method. R and
Stan code is provided, and the methods are implemented in the multinma R
package.
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