BACKGROUND: Increasing the proportion of subjects allocated to the experimental treatment in controlled clinical trials is often advocated as a method of increasing recruitment rates and improving the performance of trials. The presumption is that the higher likelihood of randomization to the experimental treatment will be perceived by potential study enrollees as an added benefit of participation and will increase recruitment rates and speed the completion of trials. However, studies with alternative allocation ratios require a larger sample size to maintain statistical power, which may result in a net increase in time required to complete recruitment and a net increase in total trial cost. PURPOSE: To describe the potential net effect of alternative allocation ratios on recruitment time and trial cost. METHODS: Models of recruitment time and trial cost were developed and used to compare trials with 1:1 allocation to trials with alternative allocation ratios under a range of per subject costs, per day costs, and enrollment rates. RESULTS: In regard to time required to complete recruitment, alternative allocation ratios are net beneficial if the recruitment rate improves by more than about 4% for trials with a 1.5:1 allocation ratio and 12% for trials with a 2:1 allocation ratio. More substantial improvements in recruitment rate, 13 and 47% respectively for scenarios we considered, are required for alternative allocation to be net beneficial in terms of tangible monetary cost. LIMITATIONS: The cost models were developed expressly for trials comparing proportions or means across treatment groups. CONCLUSIONS: Using alternative allocation ratio designs to improve recruitment may or may not be time and cost-effective. Using alternative allocation for this purpose should only be considered for trial contexts where there is both clear evidence that the alternative design does improve recruitment rates and the attained time or cost efficiency justifies the added study subject burden implied by a larger sample size.
BACKGROUND: Increasing the proportion of subjects allocated to the experimental treatment in controlled clinical trials is often advocated as a method of increasing recruitment rates and improving the performance of trials. The presumption is that the higher likelihood of randomization to the experimental treatment will be perceived by potential study enrollees as an added benefit of participation and will increase recruitment rates and speed the completion of trials. However, studies with alternative allocation ratios require a larger sample size to maintain statistical power, which may result in a net increase in time required to complete recruitment and a net increase in total trial cost. PURPOSE: To describe the potential net effect of alternative allocation ratios on recruitment time and trial cost. METHODS: Models of recruitment time and trial cost were developed and used to compare trials with 1:1 allocation to trials with alternative allocation ratios under a range of per subject costs, per day costs, and enrollment rates. RESULTS: In regard to time required to complete recruitment, alternative allocation ratios are net beneficial if the recruitment rate improves by more than about 4% for trials with a 1.5:1 allocation ratio and 12% for trials with a 2:1 allocation ratio. More substantial improvements in recruitment rate, 13 and 47% respectively for scenarios we considered, are required for alternative allocation to be net beneficial in terms of tangible monetary cost. LIMITATIONS: The cost models were developed expressly for trials comparing proportions or means across treatment groups. CONCLUSIONS: Using alternative allocation ratio designs to improve recruitment may or may not be time and cost-effective. Using alternative allocation for this purpose should only be considered for trial contexts where there is both clear evidence that the alternative design does improve recruitment rates and the attained time or cost efficiency justifies the added study subject burden implied by a larger sample size.
Authors: N Urban; S Self; L Kessler; R Prentice; M Henderson; D Iverson; D Thompson; D Byar; W Insull; S L Gorbach Journal: Control Clin Trials Date: 1990-04
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Authors: Andrea M Denicoff; Worta McCaskill-Stevens; Stephen S Grubbs; Suanna S Bruinooge; Robert L Comis; Peggy Devine; David M Dilts; Michelle E Duff; Jean G Ford; Steven Joffe; Lidia Schapira; Kevin P Weinfurt; Margo Michaels; Derek Raghavan; Ellen S Richmond; Robin Zon; Terrance L Albrecht; Michael A Bookman; Afshin Dowlati; Rebecca A Enos; Mona N Fouad; Marjorie Good; William J Hicks; Patrick J Loehrer; Alan P Lyss; Steven N Wolff; Debra M Wujcik; Neal J Meropol Journal: J Oncol Pract Date: 2013-10-15 Impact factor: 3.840