title: Identification and assessment of ongoing trials in health technology assessment reviews.
abstract: OBJECTIVE
  To assess the importance of ongoing trials in health technology assessment reviews (HTARs) for the National Institute for Clinical Excellence and to provide practical recommendations for identifying ongoing trials and assessing their possible impact.
  
  
  METHODS
  Electronic databases.
  
  
  METHODS
  Ongoing trials (or trials in progress) were defined as any trials that have started but where the results are not yet available or only interim results are available for HTARs. This methodological review included: (1) an assessment of ongoing trials in HTARs completed by the end of August 2002, (2) a survey and assessment of trial registers and other sources of ongoing trials and (3) a summary and assessment of available methods for assessing the possible impacts of ongoing trials.
  
  
  RESULTS
  The identification of ongoing trials is a common phenomenon in reviews of health technology assessment. Twenty-three of the 32 HTARs identified one or more ongoing trials and in eight of these the information on identified ongoing trials was not considered in the evidence synthesis and research recommendations. All but one HTAR that considered the potential impact of ongoing trials adopted a narrative approach. Trial registers and grey literature are important sources of information on ongoing trials. All 32 HTARs explicitly or implicitly searched for unpublished studies, and/or ongoing trials and/or grey literature and trial registers. The assessment of six commonly used trial registers suggested that most registers provided sufficient information for reviewers to decide the relevance of identified ongoing trials. However, it is sometimes extremely difficult to know whether ongoing trials identified from different sources (registers) are the same trials or belong to the same multicentre trials. The ISRCTN (the International Standard Randomised Controlled Trial Number) is the most reliable system but it has not been widely adopted. The qualitative assessment of ongoing trials compared major features of completed and ongoing trials, providing information about the possible impact of ongoing trials in terms of relevance, validity, reliability and generalisability. Quantitative methods to assess the impact of ongoing trials include cumulative meta-analysis related methods, fail-safe N, Bayesian data monitoring, and Bayesian interim predictions. The most useful method may be the Bayesian predictive probability, which estimates predictive probabilities for any possible values of treatment effect. A case study indicated that the appropriate use of quantitative methods would strengthen findings from narrative assessment of possible impact of ongoing trials.
  
  
  CONCLUSIONS
  Identification of ongoing trials is common in HTARs. Searching for ongoing trials in effectiveness reviews should be more thorough and explicit. Conversely, primary researchers, in particular those working with in multicentre trials, should label ongoing trials more clearly, preferably by ISRCTN. Qualitative assessment of identified ongoing trials is crucial and informative. Available quantitative methods could be used to strengthen findings from narrative assessment, although further research and more empirical examples are required. Information from ongoing trials may contribute to syntheses of results, conclusions and recommendations for future research. Future research is suggested into the identification and assessment of ongoing trials in other systematic reviews of effectiveness of health care interventions; existing and new methods for incorporating information on ongoing trials; comparing estimated impacts with the actual results of ongoing trials; and to incorporate findings from the assessment of ongoing trials into decision models.
authors.affiliation: Institute of Health, University of East Anglia, Norwich, UK.
authors.forename: F J
authors.initials: FJ
authors.lastname: Song
authors.forename: A
authors.initials: A
authors.lastname: Fry-Smith
authors.forename: C
authors.initials: C
authors.lastname: Davenport
authors.forename: S
authors.initials: S
authors.lastname: Bayliss
authors.forename: Y
authors.initials: Y
authors.lastname: Adi
authors.forename: J S
authors.initials: JS
authors.lastname: Wilson
authors.forename: C
authors.initials: C
authors.lastname: Hyde
mesh_terms: D001499:Bayes Theorem; D020811:Biomedical Technology / Q000639:trends*; D002986:Clinical Trials as Topic / Q000145:classification / Q000706:statistics & numerical data*; D006801:Humans; D015201:Meta-Analysis as Topic; D012042:Registries