Non-inferiority and networks: inferring efficacy from a web of data.
In the absence of placebo-controlled trials, the efficacy of a test treatment can be alternatively examined by showing its non-inferiority to an active control; that is, the test treatment is not worse than the active control by a pre-specified margin. The margin is based on the effect of the active control over placebo in historical studies. In other words, the non-inferiority setup involves a network of direct and indirect comparisons between test treatment, active controls, and placebo. Given this framework, we consider a Bayesian network meta-analysis that models the uncertainty and heterogeneity of the historical trials into the non-inferiority trial in a data-driven manner through the use of the Dirichlet process and power priors. Depending on whether placebo was present in the historical trials, two cases of non-inferiority testing are discussed that are analogs of the synthesis and fixed-margin approach. In each of these cases, the model provides a more reliable estimate of the control given its effect in other trials in the network, and, in the case where placebo was only present in the historical trials, the model can predict the effect of the test treatment over placebo as if placebo had been present in the non-inferiority trial. It can further answer other questions of interest, such as comparative effectiveness of the test treatment among its comparators. More importantly, the model provides an opportunity for disproportionate randomization or the use of small sample sizes by allowing borrowing of information from a network of trials to draw explicit conclusions on non-inferiority.
['Lin|Junjing|J|', 'Gamalo-Siebers|Margaret|M|', 'Tiwari|Ram|R|']
D001499:Bayes Theorem D002986:Clinical Trials as Topic D006801:Humans D016896:Treatment Outcome D035501:Uncertainty
meta-analysis power prior comparative effectiveness Dirichlet process prior model selection