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Adaptive design methods in clinical trials – a review

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TLDR
Several commonly considered adaptive designs in clinical trials are reviewed and some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given.
Abstract
In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, adaptive designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective adaptive designs. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered adaptive designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of adaptive design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing adaptive design methods in clinical trials are also discussed.

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A tutorial on pilot studies: the what, why and how

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A "SMART" Design for Building Individualized Treatment Sequences

TL;DR: The Sequential Multiple Assignment Randomized Trial (SMART) is reviewed and its advantages in constructing and revising adaptive interventions as compared to alternative experimental designs are highlighted.
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67A Designed extension of studies based on conditional power

TL;DR: A flexible method of extending a study based on conditional power, where the significance of the treatment difference at the planned end is used to determine the number of additional observations needed and the critical value necessary after accruing those additional observations.
References
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Journal ArticleDOI

Continual reassessment method: a practical design for phase 1 clinical trials in cancer.

TL;DR: A new approach to the design and analysis of Phase 1 clinical trials in cancer and a particularly simple model is looked at that enables the use of models whose only requirements are that locally they reasonably well approximate the true probability of toxic response.
Book

Group Sequential Methods with Applications to Clinical Trials

TL;DR: A short history of sequential and group sequential methods can be found in this paper, where the authors present a road map for the application of two-sided tests for comparing two treatments with normal response of known variance.
Journal ArticleDOI

Forcing a sequential experiment to be balanced

TL;DR: In this paper, a new method of assigning the subjects which tends to balance the experiment, but at the same time is not over vulnerable to various common forms of experimental bias is discussed.
Journal ArticleDOI

Adaptive sample size calculations in group sequential trials.

TL;DR: A method for group sequential trials that is based on the inverse normal method for combining the results of the separate stages is proposed, which enables data-driven sample size reassessments during the course of the study.
Journal ArticleDOI

Modification of Sample Size in Group Sequential Clinical Trials

TL;DR: A new group sequential test procedure is developed by modifying the weights used in the traditional repeated significance two-sample mean test, which has the type I error probability preserved at the target level and can provide a substantial gain in power with the increase of sample size.
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