Using re-randomization to increase the recruitment rate in clinical trials – an assessment of three clinical areas. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Kahan, Brennan
• Is Part Of: Trials. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-12
• Subjects: Re-randomization -- Randomized controlled trials -- Recruitment -- Efficient trial design; Group-randomized trials -- Periodicals; Randomized Controlled Trials -- Periodicals; 615.0727
• DOI: 10.1186/s13063-016-1736-z
• Languages: English
• ISSNs: 1745-6215

Abstract

Abstract Background Patient recruitment in clinical trials is often challenging, and as a result, many trials are stopped early due to insufficient recruitment. The re-randomization design allows patients to be re-enrolled and re-randomized for each new treatment episode that they experience. Because it allows multiple enrollments for each patient, this design has been proposed as a way to increase the recruitment rate in clinical trials. However, it is unknown to what extent recruitment could be increased in practice. Methods We modelled the expected recruitment rate for parallel-group and re-randomization trials in different settings based on estimates from real trials and datasets. We considered three clinical areas: in vitro fertilization, severe asthma exacerbations, and acute sickle cell pain crises. We compared the two designs in terms of the expected time to complete recruitment, and the sample size recruited over a fixed recruitment period. Results Across the different scenarios we considered, we estimated that re-randomization could reduce the expected time to complete recruitment by between 4 and 22 months (relative reductions of 19% and 45%), or increase the sample size recruited over a fixed recruitment period by between 29% and 171%. Re-randomization can increase recruitment most for trials with a short follow-up period, a long trial recruitment duration, and patients with high rates of treatment episodes. Conclusions Re-randomization has the potential to increase the recruitment rate in certain settings, and could lead to quicker and more efficient trials in these scenarios.