scholarly journals Bayesian Adaptive Randomization Designs for Clinical Trial

2015 ◽  
Vol 15 (2) ◽  
pp. 374-376
Author(s):  
Busaba Supawattan ◽  
Lily Ingsrisawa
Keyword(s):  
Clinical Trial ◽  
Adaptive Randomization

Abstract 128: Can Response Adaptive Randomization Increase Participation in Acute Stroke Trials?

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Jason Tehranisa ◽  
William J Meurer
Keyword(s):  
Clinical Trial ◽  
Acute Stroke ◽  
Research Participation ◽  
Cross Sectional Survey ◽  
Multivariable Logistic Regression Model ◽  
Cross Sectional ◽  
Absolute Increase ◽  
Adaptive Randomization ◽  
The Impact ◽  
Stroke Trial

Introduction: Acute stroke trials may be improved by using response-adaptive randomization (RAR) because it works in favor of the trial population on average. With RAR, the ratio of participants assigned to each trial arm is adjusted to favor the better performing treatment using outcome information from earlier participants in the clinical trial. Hypothesis: When presented a hypothetical acute stroke trial, more patients would agree to a RAR versus a standard clinical trial with all other aspects of trial held constant. Methods: This cross-sectional survey included adult ED patients presenting without stroke or other critical illness. A standardized protocol was used and subjects were randomized to view either an RAR or standard hypothetical acute stroke trial. After viewing the video describing the hypothetical trial (http://youtu.be/cKIWduCaPZc), reviewing the consent form, and having questions answered, subjects indicated whether they would consent to the trial. Adequacy of the informed consent process was measured by ICQ-4. A multivariable logistic regression model was fitted to estimate the impact of RAR, while controlling for demographic factors and patient understanding of the design. Results: A total of 418 subjects (210 standard 208 RAR) were enrolled. All baseline characteristics were balanced between groups. There was significantly higher participation in the RAR trial (67.3%) versus the standard trial (54.5%), absolute increase: 12.8% (95% CI: 3.7 to 22.2%). The trials were generally well understood by the participants (Table); however standard randomization appeared to be better understood. The RAR group had a higher odds ratio of agreeing to research (O.R. 1.89, 95% CI [1.2 - 2.9]), while adjusting for patient level factors. Conclusion: The RAR trial attracted more research participation than standard randomization and has potential to increase recruitment and offer benefit to future trial participants.

Bayesian adaptive randomization in a clinical trial to identify new regimens for MDR-TB: the endTB trial

2016 ◽  
Vol 20 (12) ◽  
pp. 8-12 ◽  
Author(s):  
M. Cellamare ◽  
M. Milstein ◽  
S. Ventz ◽  
E. Baudin ◽  
L. Trippa ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Adaptive Randomization ◽  
Mdr Tb

scholarly journals Optimizing Communication of Emergency Response Adaptive Randomization Clinical Trials to Potential Participants

2016 ◽  
Author(s):  
Bredan McEvoy ◽  
David Haidar ◽  
Jason Tehranisa ◽  
William J. Meurer
Keyword(s):  
Clinical Trial ◽  
Interactive Video ◽  
Clinical Trial Design ◽  
Altered Mental Status ◽  
Future Research ◽  
Correct Identification ◽  
Cross Sectional ◽  
Absolute Increase ◽  
Adaptive Randomization ◽  
Allocation Method

AbstractIntroductionAcute clinical stroke trials are challenging to communicate to patients and families considering participation. Response adaptive randomization (RAR) is a technique that alters the proportion of trial subjects receiving active treatment, based on the outcomes of previous subjects. We aimed to determine how well interactive videos would improve understanding of a simulated acute stroke trial scenario that incorporated a design with RAR.MethodsWe performed a cross-sectional study of emergency department patients who were without stroke, altered mental status, or critical illness. Subjects viewed a hypothetical stroke and clinical trial scenario. They were randomized into one of four groups with either an RAR or fixed randomization clinical trial design and with either a standard consent video, or an interactive video.Results:We enrolled 720 participants. In the RAR group with interactive video, 128 out of 149 (85.9%) of the subjects were able to correctly identify the allocation method, compared to the 172 out of 285 (61.6%) in the RAR group with the uninterrupted video for an absolute increase of 25.6% (95% Cl 17-33%). The RAR group with interactive video had a higher odds of correct identification of allocation method (O.R. 2.767, 95% Cl [1.011 – 7.570] while controlling for age, sex, ethnicity, education, self-reported understanding of protocol, stroke awareness and agreement to participate in trial.Conclusions:The interactive video increased participant understanding of an RAR design in a simulated stroke scenario. Future research should focus on whether acute trial recruitment can be enhanced using similar techniques.

Randomization

2018 ◽  
pp. 87-104
Author(s):  
Juliana C Ferreira ◽  
Ben M. W. Illigens ◽  
Felipe Fregni
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Randomized Clinical Trial ◽  
Selection Bias ◽  
Allocation Concealment ◽  
Small Sample ◽  
Adaptive Randomization ◽  
Entire Process ◽  
Advantages And Disadvantages ◽  
Small Sample Sizes

Chapter 5 gives the reader an overview of a major important feature in every randomized clinical trial: the process of randomization. This chapter describes the main features of randomization, its importance, advantages, and disadvantages. It also discusses the most common methods of randomization (simple randomization, blocked randomization, stratified randomization, adaptive randomization), as well as what the investigator should take into consideration when choosing among these options. In this scenario, the challenges of defining a method in clinical trials with small sample sizes are also discussed. Additionally, the chapter explores the consequences that may arise from lack of randomization, such as selection bias. It also focuses on defining allocation concealment and its importance to the appropriate conduction of a study. In this chapter, the reader is taken through the entire process—from choosing a suitable randomization option to ensuring the appropriate implementation of the selected method.

Evaluation of adaptive randomization for a small scale clinical trial

1981 ◽  
Vol 2 (1) ◽  
pp. 85
Author(s):  
Dennis M. Black ◽  
Anna M. Bagniewska ◽  
Stephen B. Hulley ◽  
Kjeld Molvig ◽  
Byron W. Brown
Keyword(s):  
Clinical Trial ◽  
Small Scale ◽  
Adaptive Randomization

Adaptive randomization biased coin design: Experience in a cooperative group clinical trial

1980 ◽  
Vol 1 (2) ◽  
pp. 175 ◽  
Author(s):  
John F. Hannigan ◽  
Maria M. Koretz ◽  
Ed McGuigan ◽  
Bradley Efron ◽  
Byron W. Brown
Keyword(s):  
Clinical Trial ◽  
Cooperative Group ◽  
Adaptive Randomization ◽  
Biased Coin Design ◽  
Design Experience ◽  
Biased Coin

Time trends with response-adaptive randomization: The inevitability of inefficiency

2022 ◽  
pp. 174077452110657
Author(s):  
Edward L Korn ◽  
Boris Freidlin
Keyword(s):  
Clinical Trial ◽  
Randomized Clinical Trial ◽  
Patient Population ◽  
Time Trends ◽  
Time Trend ◽  
Outcome Data ◽  
Time Block ◽  
Adaptive Randomization ◽  
Analysis Methods ◽  
Randomization Analysis

Response-adaptive randomization, which changes the randomization ratio as a randomized clinical trial progresses, is inefficient as compared to a fixed 1:1 randomization ratio in terms of increased required sample size. It is also known that response-adaptive randomization leads to biased treatment effects if there are time trends in the accruing outcome data, for example, due to changes in the patient population being accrued, evaluation methods, or concomitant treatments. Response-adaptive-randomization analysis methods that account for potential time trends, such as time-block stratification or re-randomization, can eliminate this bias. However, as shown in this Commentary, these analysis methods cause a large additional inefficiency of response-adaptive randomization, regardless of whether a time trend actually exists.

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