title: Aggregating evidence about the positive and negative effects of treatments.
abstract: OBJECTIVE
  Evidence-based decision making is becoming increasingly important in healthcare. Much valuable evidence is in the form of the results from clinical trials that compare the relative merits of treatments. In this paper, we present a new framework for representing and synthesizing knowledge from clinical trials involving multiple outcome indicators.
  
  
  METHODS
  The framework generates and evaluates arguments for claiming that one treatment is superior, or equivalent, to another based on the available evidence. 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 attacks arguments that are based on weaker evidence.
  
  
  RESULTS
  We evaluated the framework with respect to the aggregation of evidence undertaken in three published clinical guidelines that involve 56 items of evidence and 16 treatments. For each of the three guidelines, the treatment we identified as being superior using our method is a recommended treatment in the corresponding guideline.
  
  
  CONCLUSIONS
  The framework offers a formal approach to aggregating clinical evidence, taking into account subjective criteria such as preferences over outcome indicators. In the evaluation, the aggregations obtained showed a good correspondence with published clinical guidelines. Furthermore, preliminary computational studies indicate that the approach is viable for the size of evidence tables normally encountered in practice.
authors.affiliation: Department of Computer Science, University College London, London WC1E 6BT, UK. a.hunter@cs.ucl.ac.uk
authors.forename: Anthony
authors.initials: A
authors.lastname: Hunter
authors.forename: Matthew
authors.initials: M
authors.lastname: Williams
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003362:Cost-Benefit Analysis; D003657:Decision Making; D019317:Evidence-Based Medicine / Q000295:instrumentation*; D006801:Humans; D057240:Patient Preference