Reporting of statistically significant results at ClinicalTrials.gov for completed superiority randomized controlled trials

BMC Medicine

Research Article

Carolina Riveros¹  Agnes Dechartres²  Philippe Ravaud³  Ignacio Atal⁴  Jordan Scheer⁵  Elizabeth G. Bond⁶ 

[1] Centre de Recherche Epidémiologie et Statistique, INSERM U1153, Paris, France;Centre d’Epidémiologie Clinique, Hôpital Hôtel-Dieu, Assistance Publique-Hôpitaux de Paris, Paris, France;Centre de Recherche Epidémiologie et Statistique, INSERM U1153, Paris, France;Centre d’Epidémiologie Clinique, Hôpital Hôtel-Dieu, Assistance Publique-Hôpitaux de Paris, Paris, France;Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France;Cochrane France, Paris, France;Centre de Recherche Epidémiologie et Statistique, INSERM U1153, Paris, France;Centre d’Epidémiologie Clinique, Hôpital Hôtel-Dieu, Assistance Publique-Hôpitaux de Paris, Paris, France;Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France;Cochrane France, Paris, France;Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA;Centre de Recherche Epidémiologie et Statistique, INSERM U1153, Paris, France;Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France;Cochrane France, Paris, France;Centre d’Epidémiologie Clinique, Hôpital Hôtel-Dieu, Assistance Publique-Hôpitaux de Paris, Paris, France;Cochrane France, Paris, France;Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA;

关键词: Clinical trials;    Reporting;    Publication bias;    Registration;    Results;    Transparency;   

DOI  :  10.1186/s12916-016-0740-1

 received in 2016-09-13, accepted in 2016-11-03,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundPublication bias and other reporting bias have been well documented for journal articles, but no study has evaluated the nature of results posted at ClinicalTrials.gov. We aimed to assess how many randomized controlled trials (RCTs) with results posted at ClinicalTrials.gov report statistically significant results and whether the proportion of trials with significant results differs when no treatment effect estimate or p-value is posted.MethodsWe searched ClinicalTrials.gov in June 2015 for all studies with results posted. We included completed RCTs with a superiority hypothesis and considered results for the first primary outcome with results posted. For each trial, we assessed whether a treatment effect estimate and/or p-value was reported at ClinicalTrials.gov and if yes, whether results were statistically significant. If no treatment effect estimate or p-value was reported, we calculated the treatment effect and corresponding p-value using results per arm posted at ClinicalTrials.gov when sufficient data were reported.ResultsFrom the 17,536 studies with results posted at ClinicalTrials.gov, we identified 2823 completed phase 3 or 4 randomized trials with a superiority hypothesis. Of these, 1400 (50%) reported a treatment effect estimate and/or p-value. Results were statistically significant for 844 trials (60%), with a median p-value of 0.01 (Q1-Q3: 0.001–0.26). For the 1423 trials with no treatment effect estimate or p-value posted, we could calculate the treatment effect and corresponding p-value using results reported per arm for 929 (65%). For 494 trials (35%), p-values could not be calculated mainly because of insufficient reporting, censored data, or repeated measurements over time. For the 929 trials we could calculate p-values, we found statistically significant results for 342 (37%), with a median p-value of 0.19 (Q1-Q3: 0.005–0.59).ConclusionsHalf of the trials with results posted at ClinicalTrials.gov reported a treatment effect estimate and/or p-value, with significant results for 60% of these. p-values could be calculated from results reported per arm at ClinicalTrials.gov for only 65% of the other trials. The proportion of significant results was much lower for these trials, which suggests a selective posting of treatment effect estimates and/or p-values when results are statistically significant.

【 授权许可】

CC BY   
© The Author(s). 2016