Using conditional power of network meta‐analysis (NMA) to inform the design of future clinical trials. Issue 6 (16th September 2014)

• Record Type: Journal Article
• Authors: Nikolakopoulou, Adriani; Mavridis, Dimitris; Salanti, Georgia
• Is Part Of: Biometrical journal. Volume 56:Issue 6(2014:Nov.)
• Page Start: 973
• Page End: 990
• Publication Date: 2014-09-16
• Subjects: Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201300216
• Languages: English
• ISSNs: 0323-3847

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Clinical trials are typically designed with an aim to reach sufficient power to test a hypothesis about relative effectiveness of two or more interventions. Their role in informing evidence‐based decision‐making demands, however, that they are considered in the context of the existing evidence. Consequently, their planning can be informed by characteristics of relevant systematic reviews and meta‐analyses. In the presence of multiple competing interventions the evidence base has the form of a network of trials, which provides information not only about the required sample size but also about the interventions that should be compared in a future trial. In this paper we present a methodology to evaluate the impact of new studies, their information size, the comparisons involved, and the anticipated heterogeneity on the conditional power (<italic>CP</italic>) of the updated network meta‐analysis. The methods presented are an extension of the idea of <italic>CP</italic> initially suggested for a pairwise meta‐analysis and we show how to estimate the required sample size using various combinations of direct and indirect evidence in future trials. We apply the methods to two previously published networks and we show that <italic>CP</italic> for a treatment comparison is dependent on the magnitude of heterogeneity and the ratio of direct to indirect information in existing and future trials for that comparison. Our methodology can help investigators calculate the required sample size under different assumptions about heterogeneity and make decisions about the number and design of future studies (set of treatments compared).</p> </abstract>