title: A test for reporting bias in trial networks: simulation and case studies.
abstract: BACKGROUND
  Networks of trials assessing several treatment options available for the same condition are increasingly considered. Randomized trial evidence may be missing because of reporting bias. We propose a test for reporting bias in trial networks.
  
  
  METHODS
  We test whether there is an excess of trials with statistically significant results across a network of trials. The observed number of trials with nominally statistically significant p-values across the network is compared with the expected number. The performance of the test (type I error rate and power) was assessed using simulation studies under different scenarios of selective reporting bias. Examples are provided for networks of antidepressant and antipsychotic trials, where reporting biases have been previously demonstrated by comparing published to Food and Drug Administration (FDA) data.
  
  
  RESULTS
  In simulations, the test maintained the type I error rate and was moderately powerful after adjustment for type I error rate, except when the between-trial variance was substantial. In all, a positive test result increased moderately or markedly the probability of reporting bias being present, while a negative test result was not very informative. In the two examples, the test gave a signal for an excess of statistically significant results in the network of published data but not in the network of FDA data.
  
  
  CONCLUSIONS
  The test could be useful to document an excess of significant findings in trial networks, providing a signal for potential publication bias or other selective analysis and outcome reporting biases.
authors.affiliation: INSERM U1153, Hôpital Hôtel-Dieu, 1 place du Parvis Notre-Dame, 75004 Paris, France. ludovic.trinquart@htd.aphp.fr.
authors.forename: Ludovic
authors.initials: L
authors.lastname: Trinquart
authors.forename: John P A
authors.initials: JP
authors.lastname: Ioannidis
authors.forename: Gilles
authors.initials: G
authors.lastname: Chatellier
authors.forename: Philippe
authors.initials: P
authors.lastname: Ravaud
mesh_terms: D000928:Antidepressive Agents / Q000627:therapeutic use*; D014150:Antipsychotic Agents / Q000627:therapeutic use*; D006801:Humans; D015201:Meta-Analysis as Topic; D017594:Publication Bias; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D015983:Selection Bias
chemical_list: D000928:Antidepressive Agents; D014150:Antipsychotic Agents