PNS146 BEYOND DEFAULTS - ADAPTING PRINCIPLED PRIOR SELECTION IN BAYESIAN NETWORK META-ANALYSIS

Abstract

Bayesian methods provide a logical and consistent decision making framework to guide evidence synthesis and commonly used for Network Meta-Analysis (NMA). Bayesian analyses allow for estimation of rare events and can protect against issues of multiple comparisons through incorporation of genuine prior information. We adapt recent guidance documents for principled prior selection to NMA. We provide a summary of recent publications emphasizing the importance of prior selection for leveraging the full benefits of a Bayesian decision making framework. We then adapt and provide a worked example of methods based on simulation from the prior predictive distribution to facilitate understanding of the implication of priors on decision making. Standard default priors recommended in NMA methods guidance documents for health technology assessment imply unrealistic bounds for ratio-based measures and impossible values for continuous summaries like the mean difference. Prior predictive distributions for these statistics imply that prior belief places more weight on implausibly large values than are commonly seen across a variety of domains. A worked example in neonatology shows how the use visualizations of the prior predictive distribution can be incorporated in the protocol development stage in order to set priors for analysis that respect genuine expert domain knowledge. We show that conclusions of NMAs where any node is sparsely informed can differ when reasonable priors are selected. Bayesian inference allows for the incorporation of prior information with data in order to support rational decision-making. The use of standard, default “vague” priors in an effort to “let the data speak for themselves” is likely to be inconsistent with genuine domain knowledge and could meaningfully change conclusions of NMAs. Priors chosen at the protocol development stage based on visualizations of the prior predictive distribution may lead to more consistent decisions.