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

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.

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A Treatment Allocation Procedure for Sequential Clinical Trials

Biometrics ◽

10.2307/2530497 ◽

1980 ◽

Vol 36 (1) ◽

pp. 81 ◽

Cited By ~ 144

Author(s):

Colin B. Begg ◽

Boris Iglewicz

Keyword(s):

Clinical Trials ◽

Treatment Allocation ◽

Allocation Procedure

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The power of the biomarker for cervical cancer control

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.11001 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 11001-11001

Author(s):

N. S. Markovic ◽

O. Markovic ◽

E. S. Henderson

Keyword(s):

Cervical Cancer ◽

Clinical Trials ◽

Pap Smear ◽

Digital Images ◽

Papanicolaou Test ◽

New Device ◽

Comparison Results ◽

Digital Evaluation ◽

In Vitro Diagnostic

11001 Background: At ASCO 2001, the Cervical Acid Phosphatase - Papanicolaou Test (CPT) was introduced as a biomarker-based technology as a potential in vitro diagnostic device. At ASCO 2004 and 2005, we reported results from clinical trials on 2,000 subject/specimens. In a setting of standard cervical cancer screening, the safety and effectiveness of CPT (MarkPap® Test) was compared with control Pap smear and LBP. The favorable results motivated us to expand this translational research in areas where this biomarker is crucial: self-sampling and digital evaluation. We report a pilot study designed to assess whether upgrading CPT with digital image processing (IT technology) could create a new device for an effective telecytopathology. Methods: Material: Cervical specimens self-obtained by volunteers, specimens collected in prior clinical trials (Library of MarkPap slides), and control slides. Instruments and methods: We have assembled a set of instruments and procedures to create images from microscopic slides, to convert analog into digital images, to capture and store digital images and clinical data in computer files, to transfer image files via Internet to a central server for evaluation by cytotechnologist/pathologists, and to return the reports within hours. At the end, the results of reading digital and direct images were available for comparison. Results: Since each instrument was “fit-for-use”, we have tested different “conditions-for-use” for providing digital images comparable to direct images. Images of self-sampled specimens, controls and existing slides were processed through a new setup of five modules: image acquisition, image and data transferring, image evaluation, results reporting and quality control. It was made possible by upgrading software. There was no difference between results obtained after evaluation of digital or direct images. Conclusion: Using CPT and available instruments and software, we have developed a prototype in vitro diagnostic medical device, which could permit expert evaluation of cervical specimens to be performed rapidly and at affordable cost for developing countries. This device should be able to use self-obtained specimens, field laboratory personnel and equipment, but to provide high-qualified evaluation within hours for an easy affordable cost. [Table: see text]

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

Journal of Probability and Statistics ◽

10.1155/2012/842369 ◽

2012 ◽

Vol 2012 ◽

pp. 1-13 ◽

Cited By ~ 4

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.

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

Statistical Methods in Medical Research ◽

10.1177/0962280214567142 ◽

2015 ◽

Vol 26 (3) ◽

pp. 1078-1092 ◽

Cited By ~ 3

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.

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