Aggregation of Clinical Evidence Using Argumentation: A Tutorial Introduction

  • Anthony Hunter
  • Matthew Williams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9521)


In this tutorial, we describe a new framework for representing and synthesizing knowledge from clinical trials involving multiple outcome indicators. The framework offers a formal approach to aggregating clinical evidence. Based on the available evidence, arguments are generated for claiming that one treatment is superior, or equivalent, to another. Evidence comes from randomized clinical trials, systematic reviews, meta-analyses, network analyses, etc. Preference criteria over arguments are used that are based on the outcome indicators, and the magnitude of those outcome indicators, in the evidence. Meta-arguments attack (i.e. they are counterarguments to) arguments that are based on weaker evidence. An evaluation criterion is used to determine which are the winning arguments, and thereby the recommendations for which treatments are superior. Our approach has an advantage over meta analyses and network analyses in that they aggregate evidence according to a single outcome indicator, whereas our approach combines evidence according to multiple outcome indicators.


Preference Relation Aggregation Process Outcome Indicator Structure Argument Grade Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Jiri Chard and Cristina Visintin for valuable feedback on this tutorial.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity College LondonLondonUK
  2. 2.Department of OncologyCharing Cross HospitalLondonUK

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