Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations
Abstract Background The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of met...
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-023-02131-z |
| _version_ | 1797274166252011520 |
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| author | Oleksandr Sverdlov Yevgen Ryeznik Volodymyr Anisimov Olga M. Kuznetsova Ruth Knight Kerstine Carter Sonja Drescher Wenle Zhao |
| author_facet | Oleksandr Sverdlov Yevgen Ryeznik Volodymyr Anisimov Olga M. Kuznetsova Ruth Knight Kerstine Carter Sonja Drescher Wenle Zhao |
| author_sort | Oleksandr Sverdlov |
| collection | DOAJ |
| description | Abstract Background The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process. Methods We considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize $$n=500$$ n = 500 patients. Statistical properties of the recruitment process and the randomization procedures were assessed using Monte Carlo simulations. The operating characteristics included time to complete recruitment, number of centers that recruited a given number of patients, several measures of treatment imbalance and estimation efficiency under a linear model for the response, the expected proportions of correct guesses under two different guessing strategies, and the expected proportion of deterministic assignments in the allocation sequence. Results Maximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance–randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients—which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness–balance tradeoff. Conclusions The choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment. |
| first_indexed | 2024-03-07T14:55:29Z |
| format | Article |
| id | doaj.art-69347b89448b4b2a859933c3f412fa5c |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-07T14:55:29Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-69347b89448b4b2a859933c3f412fa5c2024-03-05T19:28:30ZengBMCBMC Medical Research Methodology1471-22882024-02-0124112310.1186/s12874-023-02131-zSelecting a randomization method for a multi-center clinical trial with stochastic recruitment considerationsOleksandr Sverdlov0Yevgen Ryeznik1Volodymyr Anisimov2Olga M. Kuznetsova3Ruth Knight4Kerstine Carter5Sonja Drescher6Wenle Zhao7Novartis Pharmaceuticals CorporationDepartment of Pharmacy, Uppsala UniversityAmgen Ltd.Merck & Co., Inc.Liverpool Clinical Trials Centre, University of LiverpoolBoehringer-Ingelheim Pharmaceuticals IncBoehringer-Ingelheim Pharma GmbH & Co. KGMedical University of South CarolinaAbstract Background The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process. Methods We considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize $$n=500$$ n = 500 patients. Statistical properties of the recruitment process and the randomization procedures were assessed using Monte Carlo simulations. The operating characteristics included time to complete recruitment, number of centers that recruited a given number of patients, several measures of treatment imbalance and estimation efficiency under a linear model for the response, the expected proportions of correct guesses under two different guessing strategies, and the expected proportion of deterministic assignments in the allocation sequence. Results Maximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance–randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients—which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness–balance tradeoff. Conclusions The choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment.https://doi.org/10.1186/s12874-023-02131-zMulti-center clinical trialMaximum tolerated imbalanceAllocation randomnessPoisson-gamma modelRecruitment time |
| spellingShingle | Oleksandr Sverdlov Yevgen Ryeznik Volodymyr Anisimov Olga M. Kuznetsova Ruth Knight Kerstine Carter Sonja Drescher Wenle Zhao Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations BMC Medical Research Methodology Multi-center clinical trial Maximum tolerated imbalance Allocation randomness Poisson-gamma model Recruitment time |
| title | Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations |
| title_full | Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations |
| title_fullStr | Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations |
| title_full_unstemmed | Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations |
| title_short | Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations |
| title_sort | selecting a randomization method for a multi center clinical trial with stochastic recruitment considerations |
| topic | Multi-center clinical trial Maximum tolerated imbalance Allocation randomness Poisson-gamma model Recruitment time |
| url | https://doi.org/10.1186/s12874-023-02131-z |
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