title: Best practice for analysis of shared clinical trial data.
abstract: BACKGROUND
  Greater transparency, including sharing of patient-level data for further research, is an increasingly important topic for organisations who sponsor, fund and conduct clinical trials. This is a major paradigm shift with the aim of maximising the value of patient-level data from clinical trials for the benefit of future patients and society. We consider the analysis of shared clinical trial data in three broad categories: (1) reanalysis - further investigation of the efficacy and safety of the randomized intervention, (2) meta-analysis, and (3) supplemental analysis for a research question that is not directly assessing the randomized intervention.
  
  
  CONCLUSIONS
  In order to support appropriate interpretation and limit the risk of misleading findings, analysis of shared clinical trial data should have a pre-specified analysis plan. However, it is not generally possible to limit bias and control multiplicity to the extent that is possible in the original trial design, conduct and analysis, and this should be acknowledged and taken into account when interpreting results. We highlight a number of areas where specific considerations arise in planning, conducting, interpreting and reporting analyses of shared clinical trial data. A key issue is that that these analyses essentially share many of the limitations of any post hoc analyses beyond the original specified analyses. The use of individual patient data in meta-analysis can provide increased precision and reduce bias. Supplemental analyses are subject to many of the same issues that arise in broader epidemiological analyses. Specific discussion topics are addressed within each of these areas. Increased provision of patient-level data from industry and academic-led clinical trials for secondary research can benefit future patients and society. Responsible data sharing, including transparency of the research objectives, analysis plans and of the results will support appropriate interpretation and help to address the risk of misleading results and avoid unfounded health scares.
authors.affiliation: AstraZeneca, Alderley Park, Cheshire, SK10 4TG, UK.|Centre for Biostatistics, Institute of Population Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PL, UK.
authors.forename: Sally
authors.initials: S
authors.lastname: Hollis
authors.affiliation: Amgen Ltd., 240 Cambridge Science Park, Cambridge, CB4 0WD, UK.
authors.forename: Christine
authors.initials: C
authors.lastname: Fletcher
authors.affiliation: BioMarin, 10 Bloomsbury Way, London, WC1A 2SL, UK.
authors.forename: Frances
authors.initials: F
authors.lastname: Lynn
authors.affiliation: Hoffman-La Roche, Grenzacherstrasse 124, 4070, Basel, Switzerland.
authors.forename: Hans-Joerg
authors.initials: HJ
authors.lastname: Urban
authors.affiliation: Novartis Pharma AG, Basel, Switzerland.
authors.forename: Janice
authors.initials: J
authors.lastname: Branson
authors.affiliation: Hoffman-La Roche, Grenzacherstrasse 124, 4070, Basel, Switzerland.
authors.forename: Hans-Ulrich
authors.initials: HU
authors.lastname: Burger
authors.affiliation: MRC North West Hub for Trials Methodology Research, University of Liverpool, Liverpool, UK.
authors.forename: Catrin
authors.initials: C
authors.lastname: Tudur Smith
authors.affiliation: MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, Aviation House, 125 Kingsway, London, WC2B 6NH, UK.|MRC London Hub for Trials Methodology Research, Aviation House, 125 Kingsway, London, WC2B 6NH, UK.
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Sydes
authors.affiliation: Research and Development Statistics, Bayer Pharma AG, 13353, Berlin, Germany. christoph.gerlinger@bayer.com.|Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, 66421, Homburg/Saar, Germany. christoph.gerlinger@bayer.com.
authors.forename: Christoph
authors.initials: C
authors.lastname: Gerlinger
mesh_terms: D002986:Clinical Trials as Topic; D004345:Drug Industry; D006801:Humans; D033181:Information Dissemination; D015201:Meta-Analysis as Topic; D011785:Quality Assurance, Health Care