Use of research electronic data capture (REDCap) in a sequential multiple assignment randomized trial (SMART): a practical example of automating double randomization

BMC Medical Research Methodology

Research

Carol A. Lee¹  Curt Donelson²  Michelle Lore³  Danilo Gamino⁴  Liliane C. Windsor⁵ 

[1] Addiction Center, University of Michigan, North Campus Research Complex Building 16, 2800 Plymouth Rd., Room 222W, 48109, Ann Arbor, MI, USA;Data and Technology Innovation Group, University of Illinois at Urbana Champaign, 901 West University Ave, 61801, Urbana, IL, USA;Interdisciplinary Health Sciences Institute, University of Illinois at Urbana Champaign, 405 N. Mathews Ave, 61801, Urbana, IL, USA;North Jersey Community Research Initiative, 393 Central Ave, 07103, Newark, NJ, USA;School of Social Work, University of Illinois at Urbana Champaign, 1010 W. Nevada St, 61801, Urbana, IL, USA;

关键词: Research Electronic Data capture (REDCap);    Randomized controlled trials (RCT);    Adaptive interventions;    Sequential multiple assignment Randomized Trial (SMART);    Randomization;    Experimental design;    Reducing human errors;    Automation;   

DOI  :  10.1186/s12874-023-01986-6

 received in 2023-02-10, accepted in 2023-06-26,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAdaptive interventions are often used in individualized health care to meet the unique needs of clients. Recently, more researchers have adopted the Sequential Multiple Assignment Randomized Trial (SMART), a type of research design, to build optimal adaptive interventions. SMART requires research participants to be randomized multiple times over time, depending upon their response to earlier interventions. Despite the increasing popularity of SMART designs, conducting a successful SMART study poses unique technological and logistical challenges (e.g., effectively concealing and masking allocation sequence to investigators, involved health care providers, and subjects) in addition to other challenges common to all study designs (e.g., study invitations, eligibility screening, consenting procedures, and data confidentiality protocols). Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for data collection. REDCap offers unique features that support researchers’ ability to conduct rigorous SMARTs. This manuscript provides an effective strategy for performing automatic double randomization for SMARTs using REDCap.MethodsBetween January and March 2022, we conducted a SMART using a sample of adult (age 18 and older) New Jersey residents to optimize an adaptive intervention to increase COVID-19 testing uptake. In the current report, we discuss how we used REDCap for our SMART, which required double randomization. Further, we share our REDCap project XML file for future investigators to use when designing and conducting SMARTs.ResultsWe report on the randomization feature that REDCap offers and describe how the study team automated an additional randomization that was required for our SMART. An application programming interface was used to automate the double randomizations in conjunction with the randomization feature provided by REDCap.ConclusionsREDCap offers powerful tools to facilitate the implementation of longitudinal data collection and SMARTs. Investigators can make use of this electronic data capturing system to reduce errors and bias in the implementation of their SMARTs by automating double randomization.Trial registrationThe SMART study was prospectively registered at Clinicaltrials.gov; registration number: NCT04757298, date of registration: 17/02/2021.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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