Adjusted composite likelihood for robust Bayesian meta-analysis

TL;DR

This paper introduces a calibration method for composite likelihoods in Bayesian meta-analysis, improving inference accuracy for scalar parameters by adjusting the posterior distribution.

Contribution

It proposes a simple, transformation-invariant calibration technique for the marginal posterior of scalar parameters in Bayesian meta-analysis using composite likelihoods.

Findings

Calibration improves posterior accuracy for scalar parameters

Method is invariant to monotonic transformations

Applicable in medical statistics for effect measures

Abstract

A composite likelihood is a non-genuine likelihood function that allows to make inference on limited aspects of a model, such as marginal or conditional distributions. Composite likelihoods are not proper likelihoods and need therefore calibration for their use in inference, from both a frequentist and a Bayesian perspective. The maximizer to the composite likelihood can serve as an estimator and its variance is assessed by means of a suitably defined sandwich matrix. In the Bayesian setting, the composite likelihood can be adjusted by means of magnitude and curvature methods. Magnitude methods imply raising the likelihood to a constant, while curvature methods imply evaluating the likelihood at a different point by translating, rescaling and rotating the parameter vector. Some authors argue that curvature methods are more reliable in general, but others proved that magnitude methods are sufficient to recover, for instance, the null distribution of a test statistic. We propose a simple calibration for the marginal posterior distribution of a scalar parameter of interest which is invariant to monotonic and smooth transformations. This can be enough for instance in medical statistics, where a single scalar effect measure is often the target.