Bias propagation in network meta-analysis models

A meta-analysis on the potency of foot-and-mouth disease vaccines in different animal models

Whether mice can be used as a foot-and-mouth disease (FMD) model has been debated for a long time. However, the major histocompatibility complex between pigs and mice is very different. In this study, the protective effects of FMD vaccines in different animal models were analyzed by a meta-analysis. The databases PubMed, China Knowledge Infrastructure, EMBASE, and Baidu Academic were searched. For this purpose, we evaluated evidence from 14 studies that included 869 animals with FMD vaccines. A random effects model was used to combine effects using Review Manager 5.4 software. A forest plot showed that the protective effects in pigs were statistically non-significant from those in mice [MH = 0.56, 90% CI (0.20, 1.53), P = 0.26]. The protective effects in pigs were also statistically non-significant from those in guinea pigs [MH = 0.67, 95% CI (0.37, 1.21), P = 0.18] and suckling mice [MH = 1.70, 95% CI (0.10, 28.08), P = 0.71]. Non-inferiority test could provide a hypothesis that the models (mice, suckling mice and guinea pigs) could replace pigs as FMDV vaccine models to test the protective effect of the vaccine. Strict standard procedures should be established to promote the assumption that mice and guinea pigs should replace pigs in vaccine evaluation.

Twenty years of network meta-analysis: Continuing controversies and recent developments

Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.