Adaptive designs: The Swiss Army knife among clinical trial designs?

2017 ◽  
Vol 14 (5) ◽  
pp. 417-424 ◽  
Author(s):  
Frank Bretz ◽  
Paul Gallo ◽  
Willi Maurer
Keyword(s):  
Clinical Trial ◽  
Adaptive Design ◽  
Clinical Trial Data ◽  
Parameter Estimate ◽  
Information Value ◽  
Adaptive Designs ◽  
Success Rates ◽  
Linear Regression Models ◽  
Regulatory Guidance ◽  
Trace Back

There has been considerable progress in the development and implementation of adaptive designs over the past 30 years. A major driver for this class of novel designs is the possibility to increase the information value of clinical trial data to enable better decisions, leading to more efficient drug development processes and improved late-stage success rates. In the first part of this article, we review the development of adaptive designs from different perspectives. We trace back key historical papers, report on landmark adaptive design clinical trials, review major cross-industry collaborations, and highlight key regulatory guidance documents. In the second, more technical part of this article, we address the question of whether it is possible to define factors which guide the choice between a fixed or an adaptive design for a given trial. We show that in non-linear regression models with a moderate variance of the responses, the first-stage sample size of an adaptive design should be chosen sufficiently large in order to address variability in the interim parameter estimate. In conclusion, the choice between an adaptive and a fixed design depends in a sensitive manner on the specific statistical problem under investigation.

A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes

2016 ◽  
Vol 55 (01) ◽  
pp. 4-13
Author(s):  
M. Moatti ◽  
S. Zohar ◽  
W. F. Rosenberger ◽  
S. Chevret
Keyword(s):  
Clinical Trial ◽  
Multiple Myeloma ◽  
Clinical Trials ◽  
Adaptive Design ◽  
Hazard Ratio ◽  
Adaptive Designs ◽  
Stopping Rules ◽  
Phase Iii ◽  
Optimal Criterion ◽  
Fully Bayesian

SummaryBackground: Response-adaptive randomisation designs have been proposed to im -prove the efficiency of phase III randomised clinical trials and improve the outcomes of the clinical trial population. In the setting of failure time outcomes, Zhang and Rosen -berger (2007) developed a response-adaptive randomisation approach that targets an optimal allocation, based on a fixed sample size. Objectives: The aim of this research is to propose a response-adaptive randomisation procedure for survival trials with an interim monitoring plan, based on the following optimal criterion: for fixed variance of the esti -mated log hazard ratio, what allocation minimizes the expected hazard of failure? We demonstrate the utility of the design by re -designing a clinical trial on multiple myeloma. Methods: To handle continuous monitoring of data, we propose a Bayesian response-adap -tive randomisation procedure, where the log hazard ratio is the effect measure of interest. Combining the prior with the normal likelihood, the mean posterior estimate of the log hazard ratio allows derivation of the optimal target allocation. We perform a simu lationstudy to assess and compare the perform -ance of this proposed Bayesian hybrid adaptive design to those of fixed, sequential or adaptive – either frequentist or fully Bayesian – designs. Non informative normal priors of the log hazard ratio were used, as well as mixture of enthusiastic and skeptical priors. Stopping rules based on the posterior dis -tribution of the log hazard ratio were com -puted. The method is then illus trated by redesigning a phase III randomised clinical trial of chemotherapy in patients with multiple myeloma, with mixture of normal priors elicited from experts. Results: As expected, there was a reduction in the proportion of observed deaths in the adaptive vs. non-adaptive designs; this reduction was maximized using a Bayes mix -ture prior, with no clear-cut improvement by using a fully Bayesian procedure. The use of stopping rules allows a slight decrease in the observed proportion of deaths under the alternate hypothesis compared with the adaptive designs with no stopping rules. Conclusions: Such Bayesian hybrid adaptive survival trials may be promising alternatives to traditional designs, reducing the duration of survival trials, as well as optimizing the ethical concerns for patients enrolled in the trial.

scholarly journals Adaptive Design for Staggered-Start Clinical Trial

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Ao Yuan ◽  
Qizhai Li ◽  
Ming Xiong ◽  
Ming T. Tan
Keyword(s):  
Clinical Trial ◽  
Sample Size ◽  
Adaptive Design ◽  
Optimality Criterion ◽  
Total Sample ◽  
Adaptive Designs ◽  
Initial Sample ◽  
Total Sample Size ◽  
New Type ◽  
Start Trial

AbstractIn phase II and/or III clinical trial study, there are several competing treatments, the goal is to assess the performances of the treatments at the end of the study, the trial design aims to minimize risks to the patients in the trial, according to some given allocation optimality criterion. Recently, a new type of clinical trial, the staggered-start trial has been proposed in some studies, in which different treatments enter the same trial at different times. Some basic questions for this trial are whether optimality can still be kept? under what conditions? and if so how to allocate the the coming patients to treatments to achieve such optimality? Here we propose and study a class of adaptive designs of staggered-start clinical trials, in which for given optimality criterion object, we show that as long as the initial sizes at the beginning of the successive trials are not too large relative to the total sample size, the proposed design can still achieve optimality criterion asymptotically for the allocation proportions as the ordinary trials; if these initial sample sizes have about the same magnitude as the total sample size, full optimality cannot be achieved. The proposed method is simple to use and is illustrated with several examples and a simulation study.

scholarly journals Artificial Intelligence Powered Clinical Trials

2021 ◽  
Author(s):  
Ankit Majie
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Pharmaceutical Industry ◽  
Real Life ◽  
Clinical Trial Data ◽  
Success Rates ◽  
Conventional Procedure ◽  
The Future ◽  
Time Required

The future of clinical trials is changing rapidly due to the introduction of Artificial Intelligence (AI) to study the clinically significant patterns and algorithms generated upon the input from the trial. The high failure rates in the clinical trials leads to inefficient drug development cycle which increases expenses of the pharmaceutical industry. The technique of artificial intelligence allows the decision makers to study the clinical trials in real life conditions which increases the accuracy of the trials. Thus, decreasing the burden of the pharmaceutical industry and increasing the success rates of the trial. Moreover, clinical trial is a much time-consuming process involving 10-15 years for just one drug molecule with lot of investment. The use clinical trial can reduce the time required for the trial and its investment reduces to one half. With the use of the AI powered clinical trials one drug from every 100 drugs passes this phase easily with genuine results which is much greater than the conventional procedure. Rather the use of clinical trials can help in automated documentation of the clinical trial data under the database of the concerned company be retrieved and accessed very easily. The future of AI will include generation of precision medicine and even prediction of drug resistance in clinical trials.

Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

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