Doctoral candidate in Biostatistics, Zhenxun Wang, will present:

“Statistical Methods for Arm-based Bayesian Network Meta-analysis”

PhD Adviser: Haitao Chu

Abstract: Network meta-analysis (NMA) is a recently developed tool to combine and contrast direct and indirect evidence in systematic reviews of multiple treatments. The arm-based (AB) NMA approach can estimate absolute effects (such as the overall treatment-specific event rates), which are useful in medicine and public health, as well as relative effects. In AB-NMA, treatment-specific variances are needed to estimate treatment-specific overall effects, while accurate estimation of correlation coefficients is critical to allow borrowing information across treatments. However, partially due to the lack of information, the estimation of correlation coefficients and variances can be biased and unstable if we use the conjugate priors (e.g., the inverse-Wishart (IW) distribution) for the covariance matrix.

To address the first challenge of accurately estimating correlation coefficients, several separation strategies (i.e., separate priors on variances and correlations) can be considered. Based on simulation studies, a separation strategy with appropriate priors for the correlation matrix (e.g., equal correlations) performs better than the IW prior and is thus recommended as the default vague prior in the AB approach. To address the second challenge of variance estimation, we can either assume the variances of different treatments share a common distribution with unknown hyper-parameters (variance shrinkage) or in AB-NMA, borrow information from single arm studies (variance extrapolation). Overall, either variance shrinkage or variance extrapolation methods could improve estimation on variances when the number of clinical studies involving each treatment is relatively small.