title: Overview of data-synthesis in systematic reviews of studies on outcome prediction models.
abstract: BACKGROUND
  Many prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of reviews synthesize and report the results of primary outcome prediction studies.
  
  
  METHODS
  Outcome prediction reviews published in MEDLINE between October 2005 and March 2011 were eligible and 127 Systematic reviews with the aim to summarize outcome prediction studies written in English were identified for inclusion.Characteristics of the reviews and the primary studies that were included were independently assessed by 2 review authors, using standardized forms.
  
  
  RESULTS
  After consensus meetings a total of 50 systematic reviews that met the inclusion criteria were included. The type of primary studies included (prognostic factor or outcome prediction) was unclear in two-thirds of the reviews. A minority of the reviews reported univariable or multivariable point estimates and measures of dispersion from the primary studies. Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear. In most reviews there was no information about model performance. Quantitative analysis was performed in 10 reviews, and 49 reviews assessed the primary studies qualitatively. In both analyses types a range of different methods was used to present the results of the outcome prediction studies.
  
  
  CONCLUSIONS
  Different methods are applied to synthesize primary study results but quantitative analysis is rarely performed. The description of its objectives and of the primary studies is suboptimal and performance parameters of the outcome prediction models are rarely mentioned. The poor reporting and the wide variety of data synthesis strategies are prone to influence the conclusions of outcome prediction reviews. Therefore, there is much room for improvement in reviews of outcome prediction studies.
authors.affiliation: Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands. to.vandenberg@vumc.nl
authors.forename: Tobias
authors.initials: T
authors.lastname: van den Berg
authors.forename: Martijn W
authors.initials: MW
authors.lastname: Heymans
authors.forename: Stephanie S
authors.initials: SS
authors.lastname: Leone
authors.forename: David
authors.initials: D
authors.lastname: Vergouw
authors.forename: Jill A
authors.initials: JA
authors.lastname: Hayden
authors.forename: Arianne P
authors.initials: AP
authors.lastname: Verhagen
authors.forename: Henrica C W
authors.initials: HC
authors.lastname: de Vet
mesh_terms: D035843:Biomedical Research; D016208:Databases, Factual; D006801:Humans; D011237:Predictive Value of Tests; D016896:Treatment Outcome