The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials.
In the following chapter on late Phase III studies, the authors emphasize modern adaptive methods and seamless Phase II—III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis. For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits.
The authors provide many detailed examples drawing on real data sets. Scott M.
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Berry is the President and Senior Statistical Scientist at Berry Consultants, a statistical consulting group specializing in adaptive clinical trial design in pharmaceutical and medical device research and development. Bradley P. Anderson Cancer Center. We included a rule to determine if the trial should be stopped for futility, i. This is the re-optimization step. Survival times for patients in phase I-II and phase III are generated after their toxicity and efficacy are scored, which does not allow the possibility that a patient may die before their short term indicators are seen.
For each scenario and design, 5, simulation replications were performed. The simulation results are summarized in Table 5. Simulation results. The differences are extremely large in scenario 3, with an improvement of. Part of this required increase is due to the design correctly switching to the best dose of A in terms of overall survival, which decreases the likelihood that a trial will stop early by declaring C to be superior or due to futility.
This increase in required sample size and trial duration are the price paid for the much larger probability of a successful phase III trial in cases where dose switching increases mean survival time with A. This is because more information at different doses in phase I-II makes switching to the best dose in stage 2 more likely.
We exponentiated the linear term for the gamma and Weibull distribution rate parameters, but did not do this for the lognormal distribution. The means under the null and alternative hypotheses for each distribution are given in Web Table 3. Because so many patients have early failures, patients are not followed as long before the final group sequential test. Similar improvements are seen under the other distributions. This shows that the logrank test is not robust to the Weibull distribution with decreasing hazard. The type I error constraints are nearly met for each distribution.
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Robustness simulation results. We use information from all patients treated with A , including their short term efficacy and toxicity indicators, dose assigned, and survival time information, in order to more accurately select the dose of A that provides the highest posterior mean survival time.
The design is based on an assumed a mixture model for the survival time distribution that averages over the possible short term phase I-II outcomes. However, the necessary modifications of the design parameters and computer software to accommodate such a change would be non-trivial. Similarly, a complicated but straightforward extension of the methodology may address the problem of possible deaths before evaluation of Y E , Y T. Supplementary Materials. Web Appendices and tables , referenced in Sections 2, 3, and 5, are available at the Biometrics website on Wiley Online Library.
A recent approach combines the first two phases by conducting a phase I-II trial, which chooses an optimal dose based on both efficacy and toxicity, and evaluation of A at the selected optimal phase I-II dose then is done in a phase III trial. The snippet could not be located in the article text. This may be because the snippet appears in a figure legend, contains special characters or spans different sections of the article. Author manuscript; available in PMC Aug PMID: Chapple and Peter F. A Find articles by Andrew G.
A Find articles by Peter F. Chapple: ude. Thall: gro. Copyright notice. The publisher's final edited version of this article is available at Biometrics. Summary Conventionally, evaluation of a new drug, A , is done in three phases. Introduction After a new treatment agent, A , is identified in pre-clinical studies, conventional clinical drug development and evaluation is carried out in three phases Cancer. For each subsequent cohort until N F patients have been treated, use the function AssignEffTox to obtain the next dose to give.
Stop accrual after N S patients have been enrolled in the phase III portion, including patients treated with a dose that is no longer considered optimal.
Adaptive clinical trial designs in oncology - Zang - Chinese Clinical Oncology
Possible Trial Outcomes Before presenting our simulation results, we discuss possible design decisions and comment on each under different true states of nature. Open in a separate window. The design correctly selects x S opt as optimal and declares A x S opt superior to C.
While the design may pick the best dose of A , it incorrectly concludes that A at that dose is superior to C. Table 3 Eff-Tox Scenarios. Table 4 Simulation Parameters. Table 5 Simulation results. Table 6 Robustness simulation results. Supplementary Material Supplemental Click here to view. Contributor Information Andrew G.
References Arrowsmith J. Trial watch: Phase III and submission failures: Nature Review Drug Discovery. Journal of Statistical Planning and Inference. Cancer phase I clinical trials: Efficient dose escalation with overdose control. Statistics in Medicine. Clinical Development Success Rates Bryant J, Day R. Incorporating toxicity considerations into the design of two-stage phase II clinical trials. What are the phases of clinical trials?
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Statistical Design and Analysis of Clinical Trials: Principles and Methods
Statistical software for the design, simulation and monitoring of clinical trials. Cytel Inc. Green P.