Estimating the variance for heterogeneity in arm-based network meta-analysis

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Piepho, H.-P., Madden, L. V., Roger, J., Payne, R. W. and Williams, E. R. 2018. Estimating the variance for heterogeneity in arm-based network meta-analysis. Pharmaceutical Statistics. 17 (3), pp. 264-277.

AuthorsPiepho, H.-P., Madden, L. V., Roger, J., Payne, R. W. and Williams, E. R.

Network meta-analysis can be implemented by using arm-based or contrast-based models. Here we focus on arm-based models and fit them using generalized linear mixed model procedures. Full maximum likelihood (ML) estimation leads to biased trial-by-treatment interaction variance estimates for heterogeneity. Thus, our objective is to investigate alternative approaches to variance estimation that reduce bias compared with full ML. Specifically, we use penalized quasi-likelihood/pseudo-likelihood and hierarchical (h) likelihood approaches. In addition, we consider a novel model modification that yields estimators akin to the residual maximum likelihood estimator for linear mixed models. The proposed methods are compared by simulation, and 2 real datasets are used for illustration. Simulations show that penalized quasi-likelihood/pseudo-likelihood and h-likelihood reduce bias and yield satisfactory coverage rates. Sum-to-zero restriction and baseline contrasts for random trial-by-treatment interaction effects, as well as a residual ML-like adjustment, also reduce bias compared with an unconstrained model when ML is used, but coverage rates are not quite as good. Penalized quasi-likelihood/pseudo-likelihood and h-likelihood are therefore recommended.

Year of Publication2018
JournalPharmaceutical Statistics
Journal citation17 (3), pp. 264-277
Digital Object Identifier (DOI)doi:10.1002/pst.1857
Open accessPublished as non-open access
Output statusPublished
Publication dates
Online19 Apr 2018
Publication process dates
Accepted15 Feb 2018
Copyright licensePublisher copyright

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