A Treatment Allocation Procedure for Sequential Clinical Trials

Biometrics ◽  
1980 ◽  
Vol 36 (1) ◽  
pp. 81 ◽  
Author(s):  
Colin B. Begg ◽  
Boris Iglewicz
Keyword(s):  
Clinical Trials ◽  
Treatment Allocation ◽  
Allocation Procedure

Patient Registration and Treatment Allocation in Multicenter Clinical Trials Using a FAX-OCR System

1994 ◽  
Vol 33 (05) ◽  
pp. 530-534 ◽  
Author(s):  
T. Kamakura ◽  
M. Sakamoto ◽  
T. Odaka ◽  
Y. Nose ◽  
K. Akazawa
Keyword(s):  
Clinical Trials ◽  
Patient Identification ◽  
Minimization Method ◽  
Treatment Allocation ◽  
Staff Members ◽  
Patient Registration ◽  
Allocation Procedure ◽  
Multicenter Clinical Trials ◽  
Balance Treatment

Abstract:This article describes the design and results of implementation of an automated patient registration and freatment allocation system (RETAS) used in multicenter clinical trials. RETAS was developed using a FAX-OCR system by which handwritten Japanese and English characters, as well as numericals and forms with check boxes, are sent from participating institutions by Fax, processed using an optical character reader, and then transmitted to a host computer at a statistical center. Based on the facsimile data, RETAS can automatically review eligibility, collect patient identification data and provide a randomized treatment allocation. RETAS permits uninterrupted, unattended operation at a statistical center, 24 hours a day, 7 days a week. Therefore, it drastically decreases the workload of personnel at the statistical center needed to support central telephone registration coverage. Consequently, staff members are free to focus on patient registration, treatment allocation, and follow-up of patients. The treatment allocation procedure in this system is based on Pocock and Simon’s minimization method combined with Zelen’s method for institution balancing. By this system it was possible to balance treatment numbers for each level of various prognostic factors over an entire trial and, at the same time, balance the allocation of treatments within an institution. The system currently supports the protocol of a clinical trial for Adjuvant Chemo-Endocrine Therapy for Breast Cancer in West Japan.

Treatment allocation in clinical trials

Transfusion ◽  
2007 ◽  
Vol 47 (12) ◽  
pp. 2187-2188 ◽  
Author(s):  
Kathryn E. Webert
Keyword(s):  
Clinical Trials ◽  
Treatment Allocation

scholarly journals Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

2021 ◽  
Author(s):  
Elja Arjas ◽  
Dario Gasbarra
Keyword(s):  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Statistical Significance ◽  
Adaptive Designs ◽  
Critical Threshold ◽  
Attractive Alternative ◽  
Posterior Probabilities ◽  
Statistical Significance Testing ◽  
Treatment Allocation ◽  
Frequentist Paradigm

Abstract Background: Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power. Results: The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during Phase II and III. This approach is based on comparing the performance of the different treatment arm in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm (Rule 1), and treatment selection, removing an arm from the trial permanently (Rule 2). The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package. Conclusion: The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

scholarly journals Standardising and Assessing Digital Images for Use in Clinical Trials: A Practical, Reproducible Method That Blinds the Assessor to Treatment Allocation

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e110395 ◽  
Author(s):  
Asha C. Bowen ◽  
Kara Burns ◽  
Steven Y. C. Tong ◽  
Ross M. Andrews ◽  
Robyn Liddle ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Digital Images ◽  
Treatment Allocation ◽  
Reproducible Method

scholarly journals Balancing Treatment Allocation over Continuous Covariates: A New Imbalance Measure for Minimization

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Yanqing Hu ◽  
Feifang Hu
Keyword(s):  
Clinical Trials ◽  
Efficient Algorithm ◽  
Allocation Rule ◽  
Simulation Studies ◽  
Treatment Allocation ◽  
Good Balance ◽  
Loss Of Information ◽  
Continuous Covariates ◽  
Continuous Covariate ◽  
Balance Treatment

In many clinical trials, it is important to balance treatment allocation over covariates. Although a great many papers have been published on balancing over discrete covariates, the procedures for continuous covariates have been less well studied. Traditionally, a continuous covariate usually needs to be transformed to a discrete one by splitting its range into several categories. Such practice may lead to loss of information and is susceptible to misspecification of covariate distribution. The more recent papers seek to define an imbalance measure that preserves the nature of continuous covariates and set the allocation rule in order to minimize that measure. We propose a new design, which defines the imbalance measure by the maximum assignment difference when all possible divisions of the covariate range are considered. This measure depends only on ranks of the covariate values and is therefore free of covariate distribution. In addition, we developed an efficient algorithm to implement the new procedure. By simulation studies we show that the new procedure is able to keep good balance properties in comparison with other popular designs.

scholarly journals A surrogate-primary replacement algorithm for response-adaptive randomization in stroke clinical trials

2015 ◽  
Vol 26 (3) ◽  
pp. 1078-1092 ◽  
Author(s):  
Amy S Nowacki ◽  
Wenle Zhao ◽  
Yuko Y Palesch
Keyword(s):  
Clinical Trials ◽  
Acute Stroke ◽  
Computer Simulations ◽  
Primary Outcome ◽  
Expected Number ◽  
Treatment Allocation ◽  
Adaptive Randomization ◽  
Surrogate Outcome ◽  
Surrogate Outcomes ◽  
Replacement Algorithm

Response-adaptive randomization (RAR) offers clinical investigators benefit by modifying the treatment allocation probabilities to optimize the ethical, operational, or statistical performance of the trial. Delayed primary outcomes and their effect on RAR have been studied in the literature; however, the incorporation of surrogate outcomes has not been fully addressed. We explore the benefits and limitations of surrogate outcome utilization in RAR in the context of acute stroke clinical trials. We propose a novel surrogate-primary (S-P) replacement algorithm where a patient’s surrogate outcome is used in the RAR algorithm only until their primary outcome becomes available to replace it. Computer simulations investigate the effect of both the delay in obtaining the primary outcome and the underlying surrogate and primary outcome distributional discrepancies on complete randomization, standard RAR and the S-P replacement algorithm methods. Results show that when the primary outcome is delayed, the S-P replacement algorithm reduces the variability of the treatment allocation probabilities and achieves stabilization sooner. Additionally, the S-P replacement algorithm benefit proved to be robust in that it preserved power and reduced the expected number of failures across a variety of scenarios.

Sequential treatment allocation in clinical trials

Biometrika ◽  
1971 ◽  
Vol 58 (3) ◽  
pp. 419-426 ◽  
Author(s):  
B. J. FLEHINGER ◽  
T. A. LOUIS
Keyword(s):  
Clinical Trials ◽  
Sequential Treatment ◽  
Treatment Allocation

Sequential balancing: A simple method for treatment allocation in clinical trials

2005 ◽  
Vol 26 (6) ◽  
pp. 637-645 ◽  
Author(s):  
George Florimond Borm ◽  
Elizabeth H. Hoogendoorn ◽  
Martin den Heijer ◽  
Gerhard A. Zielhuis
Keyword(s):  
Clinical Trials ◽  
Simple Method ◽  
Treatment Allocation
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