Literature DB >> 23539114

From ClinicalTrials.gov trial registry to an analysis-ready database of clinical trial results.

M Soledad Cepeda1, Victor Lobanov, Jesse A Berlin.   

Abstract

BACKGROUND: The ClinicalTrials.gov web site provides a convenient interface to look up study results, but it does not allow downloading data in a format that can be readily used for quantitative analyses.
PURPOSE: To develop a system that automatically downloads study results from ClinicalTrials.gov and provides an interface to retrieve study results in a spreadsheet format ready for analysis.
METHODS: Sherlock(®) identifies studies by intervention, population, or outcome of interest and in seconds creates an analytic database of study results ready for analyses. The outcome classification algorithms used in Sherlock were validated against a classification by an expert.
CONCLUSIONS: Having a database ready for analysis that can be updated automatically, dramatically extends the utility of the ClinicalTrials.gov trial registry. It increases the speed of comparative research, reduces the need for manual extraction of data, and permits answering a vast array of questions.

Mesh:

Year:  2013        PMID: 23539114     DOI: 10.1177/1740774513475849

Source DB:  PubMed          Journal:  Clin Trials        ISSN: 1740-7745            Impact factor:   2.486


  9 in total

1.  Automatic extraction of quantitative data from ClinicalTrials.gov to conduct meta-analyses.

Authors:  Richeek Pradhan; David C Hoaglin; Matthew Cornell; Weisong Liu; Victoria Wang; Hong Yu
Journal:  J Clin Epidemiol       Date:  2018-09-23       Impact factor: 6.437

2.  Efficiency and contribution of strategies for finding randomized controlled trials: a case study from a systematic review on therapeutic interventions of chronic depression.

Authors:  Annika Westphal; Levente Kriston; Lars P Hölzel; Martin Härter; Alessa von Wolff
Journal:  J Public Health Res       Date:  2014-07-01

3.  A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov.

Authors:  Diana de la Iglesia; Miguel García-Remesal; Alberto Anguita; Miguel Muñoz-Mármol; Casimir Kulikowski; Víctor Maojo
Journal:  PLoS One       Date:  2014-10-27       Impact factor: 3.240

4.  Clinical Trials.Gov: A Topical Analyses.

Authors:  Vibha Anand; Amos Cahan; Soumya Ghosh
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2017-07-26

5.  Identifying Anticipated Events of Future Clinical Trials by Leveraging Data from the Placebo Arms of Completed Trials.

Authors:  Xiang-Lin Tan; David M Kern; M Soledad Cepeda
Journal:  Ther Innov Regul Sci       Date:  2020-11-09       Impact factor: 1.778

6.  Methodological developments in searching for studies for systematic reviews: past, present and future?

Authors:  Carol Lefebvre; Julie Glanville; L Susan Wieland; Bernadette Coles; Alison L Weightman
Journal:  Syst Rev       Date:  2013-09-25

7.  Wasted research when systematic reviews fail to provide a complete and up-to-date evidence synthesis: the example of lung cancer.

Authors:  Perrine Créquit; Ludovic Trinquart; Amélie Yavchitz; Philippe Ravaud
Journal:  BMC Med       Date:  2016-01-20       Impact factor: 8.775

8.  Second thoughts on the final rule: An analysis of baseline participant characteristics reports on ClinicalTrials.gov.

Authors:  Amos Cahan; Vibha Anand
Journal:  PLoS One       Date:  2017-11-06       Impact factor: 3.240

9.  StudyPortal - Geovisualization of Study Research Networks.

Authors:  Julian Varghese; Michael Fujarski; Martin Dugas
Journal:  J Med Syst       Date:  2019-12-10       Impact factor: 4.460

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.