Methodological survey of designed uneven randomization trials (DU-RANDOM): a protocol
1 Department of Clinical Epidemiology, 2nd Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
2 Departments of Internal Medicine, American University of Beirut, Beirut, Lebanon
3 Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada
4 Departments of Medicine, State University of New York at Buffalo, Buffalo, NY, USA
5 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
6 Faculty of Dentistry, University of Chile, Santiago, Chile
7 Department of Medical Biometry and Statistics, Albert-Ludwigs-University, Freiburg, Germany
8 Department of Biology (Physiology Specialization), McMaster University, Hamilton, ON, Canada
9 Epidemiology and Health Technology Assessment Institute, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
10 Department of Internal Medicine, Division of Nephrology, University of Missouri Kansas City, Kansas City, MO, USA
11 Basel Institute for Clinical Epidemiology & Biostatistics, University Hospital Basel, Basel, CH, Switzerland
Trials 2014, 15:33 doi:10.1186/1745-6215-15-33Published: 23 January 2014
Although even randomization (that is, approximately 1:1 randomization ratio in study arms) provides the greatest statistical power, designed uneven randomization (DUR), (for example, 1:2 or 1:3) is used to increase participation rates. Until now, no convincing data exists addressing the impact of DUR on participation rates in trials. The objective of this study is to evaluate the epidemiology and to explore factors associated with DUR.
We will search for reports of RCTs published within two years in 25 general medical journals with the highest impact factor according to the Journal Citation Report (JCR)-2010. Teams of two reviewers will determine eligibility and extract relevant information from eligible RCTs in duplicate and using standardized forms. We will report the prevalence of DUR trials, the reported reasons for using DUR, and perform a linear regression analysis to estimate the association between the randomization ratio and the associated factors, including participation rate, type of informed consent, clinical area, and so on.
A clearer understanding of RCTs with DUR and its association with factors in trials, for example, participation rate, can optimize trial design and may have important implications for both researchers and users of the medical literature.