Classifying information-sharing methods

BMC Medical Research Methodology

Marta O. Soares¹  Beth Woods¹  Stephen Palmer¹  Georgios F. Nikolaidis² 

[1] The University of York, Centre for Health Economics, Alcuin A Block, Heslington, YO10 5DD, York, UK;The University of York, Centre for Health Economics, Alcuin A Block, Heslington, YO10 5DD, York, UK;IQVIA, 210 Pentonville Road, N1 9JY, London, UK;

关键词: Meta-analysis;    Network meta-analysis;    Borrowing-strength;    Indirect evidence;    Information-sharing;   

DOI  :  10.1186/s12874-021-01292-z

来源: Springer

【 摘 要 】

BackgroundSparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly: for instance, when there is no evidence on a comparator, evidence on other treatments of the same molecular class could be used; similarly, a decision on children may borrow-strength from evidence on adults. Usually, in HTA, such indirect evidence is either included by ignoring any differences (‘lumping’) or not included at all (‘splitting’). However, a range of more sophisticated methods exists, primarily in the biostatistics literature. The objective of this study is to identify and classify the breadth of the available information-sharing methods.MethodsForwards and backwards citation-mining techniques were used on a set of seminal papers on the topic of information-sharing. Papers were included if they specified (network) meta-analytic methods for combining information from distinct populations, interventions, outcomes or study-designs.ResultsOverall, 89 papers were included. A plethora of evidence synthesis methods have been used for information-sharing. Most papers (n=79) described methods that shared information on relative treatment effects. Amongst these, there was a strong emphasis on methods for information-sharing across multiple outcomes (n=42) and treatments (n=25), with fewer papers focusing on study-designs (n=23) or populations (n=8). We categorise and discuss the methods under four ’core’ relationships of information-sharing: functional, exchangeability-based, prior-based and multivariate relationships, and explain the assumptions made within each of these core approaches.ConclusionsThis study highlights the range of information-sharing methods available. These methods often impose more moderate assumptions than lumping or splitting. Hence, the degree of information-sharing that they impose could potentially be considered more appropriate. Our identification of four ‘core’ methods of information-sharing allows for an improved understanding of the assumptions underpinning the different methods. Further research is required to understand how the methods differ in terms of the strength of sharing they impose and the implications of this for health care decisions.

【 授权许可】

CC BY