title: Scientific hypotheses can be tested by comparing the effects of one treatment over many diseases in a systematic review.
abstract: OBJECTIVE
  To describe the use of systematic reviews or overviews (systematic reviews of systematic reviews) to synthesize quantitative evidence of intervention effects across multiple indications (multiple-indication reviews) and to highlight issues pertaining to such reviews.
  
  
  METHODS
  MEDLINE was searched from 2003 to January 2014. We selected multiple-indication reviews of interventions of allopathic medicine that included evidence from randomized controlled trials. We categorized the subject areas evaluated by these reviews and examined their methodology. Utilities and caveats of multiple-indication reviews are illustrated with examples drawn from published literature.
  
  
  RESULTS
  We retrieved 52 multiple-indication reviews covering a wide range of interventions. The method has been used to detect unintended effects, improve precision by pooling results across indications, and examine scientific hypotheses across disease classes.
  
  
  CONCLUSIONS
  Systematic reviews of interventions are typically used to evaluate the effects of treatments, one indication at a time. Here, we argue that, with due attention to methodological caveats, much can be learned by comparing the effects of a given treatment across many related indications.
authors.affiliation: School of Health and Population Sciences, University of Birmingham, Birmingham B15 2TT, UK; Division of Health Sciences, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: Yen-Fu
authors.initials: YF
authors.lastname: Chen
authors.affiliation: School of Health and Population Sciences, University of Birmingham, Birmingham B15 2TT, UK.
authors.forename: Karla
authors.initials: K
authors.lastname: Hemming
authors.affiliation: School of Health and Population Sciences, University of Birmingham, Birmingham B15 2TT, UK.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Chilton
authors.affiliation: Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.
authors.forename: Keshav K
authors.initials: KK
authors.lastname: Gupta
authors.affiliation: Centre for Statistics in Medicine, University of Oxford, Oxford OX3 7LD, UK.
authors.forename: Douglas G
authors.initials: DG
authors.lastname: Altman
authors.affiliation: School of Health and Population Sciences, University of Birmingham, Birmingham B15 2TT, UK; Division of Health Sciences, University of Warwick, Coventry CV4 7AL, UK. Electronic address: r.j.lilford@warwick.ac.uk.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Lilford
mesh_terms: D006801:Humans; D017594:Publication Bias; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D012196:Review Literature as Topic; D009626:Terminology as Topic; D016896:Treatment Outcome
keywords: Assessing harms; Detecting unintended effects; Evaluating effectiveness; Multiple-indication reviews; Overviews; Panoramic meta-analyses; Research methods