Flexible Adaptive Designs Research Articles

Innovative ways of design education developing at the university

Design education is rapidly developing in unity with formation and strengthening of theoretical and methodological design basis. Changing requirements for the modern educational system provokes the search for innovative ways of development. Rapid introduction of distance education has made it possible to test experimental forms and methods of teaching design students. As a result of processing the experimental data, outdated models of the organization of the educational process, the lack of flexibility and variability of curricula and work programs of disciplines were revealed. It became necessary to adjust the educational process, taking into account modern mental characteristics of students, their levels of motivation, system of values and ideas about the profession of a designer, taking into account dynamic development of existing design directions and emergence of new specific branches. These factors determine the creation of a methodological model built into the context of design education, which should have such qualities as flexibility and adaptability to changes in the external environment. Implementation of flexible adaptive design teaching model becomes a source of innovative changes in this very external environment. To build a methodological system, the following methodological approaches were used: cognitive, reflexive, eutagogic, synergetic, personality-activity, heuristic-algorithmic, recursive-design.

410TiP - Phase 2, open-label study of ceritinib in patients (pts) with advanced non-lung solid tumors and hematological malignancies characterized by genetic abnormalities in anaplastic lymphoma kinase (ALK) using a flexible adaptive design: ASCEND-10

410TiP - Phase 2, open-label study of ceritinib in patients (pts) with advanced non-lung solid tumors and hematological malignancies characterized by genetic abnormalities in anaplastic lymphoma kinase (ALK) using a flexible adaptive design: ASCEND-10

Reflections on the adaptive designs accelerating promising trials into treatments (ADAPT-IT) process—Findings from a qualitative study

The context for this study was the Adaptive Designs Advancing Promising Treatments Into Trials (ADAPT-IT) project, which aimed to incorporate flexible adaptive designs into pivotal clinical trials and to conduct an assessment of the trial development process. Little research provides guidance to academic institutions in planning adaptive trials. The purpose of this qualitative study was to explore the perspectives and experiences of stakeholders as they reflected back about the interactive ADAPT-IT adaptive design development process, and to understand their perspectives regarding lessons learned about the design of the trials and trial development. We conducted semi-structured interviews with ten key stakeholders and observations of the process. We employed qualitative thematic text data analysis to reduce the data into themes about the ADAPT-IT project and adaptive clinical trials. The qualitative analysis revealed four themes: education of the project participants, how the process evolved with participant feedback, procedures that could enhance the development of other trials, and education of the broader research community. While participants became more likely to consider flexible adaptive designs, additional education is needed to both understand the adaptive methodology and articulate it when planning trials.

Adaptive Designs with Discrete Test Statistics and Consideration of Overrunning.

Interim analyses are used in clinical trials in order to enable early decisions for medical, ethical, and economic reasons. However, it appears unfeasible to stop a trial during such an interim analysis. New patients will thus enter the trial while the interim analysis is ongoing. Moreover, depending on the event kinetics of the specific disease, the trial design, and the corresponding endpoints, some patients might still be unevaluable at the interim analysis due to not yet completed follow-up. Occurrence of these types of patients is characteristic for sequentially analyzed trials. Such patients are referred to as interim patients. In trials with multiple primary endpoints, another type of interim patients occurs. If some but not all null hypotheses can be rejected at the interim analysis, the trial might be continued to a second stage in order to answer the remaining questions. These second stage patients, however, provide new data to all trial questions including the already rejected ones and thus formally act as interim patients regarding the already rejected null hypotheses. Although all kinds of interim patients are not part of the interim analysis, the data collected on those patients have to be sent to the office of regulatory affairs and will be analyzed. If a smaller or contrasting treatment effect is observed in interim patients, this might lead to a withdrawal of an earlier superiority proof. Presently, interim patients and their data are usually not considered in the confirmatory test. We offer a strategy to deal with interim patients in sequentially analyzed trials with discrete test statistics. The method covers sequentially analyzed single- and multi-arm trials with one or multiple primary endpoints. When planning adaptive designs, it is common practice to assume that the stage-wise p-values are independent and standard uniformly distributed under the null hypothesis. In the context of discrete test statistics, this implies conservative tests. We provide an algorithm which iteratively optimizes an initially given design while adjusting for both discreteness of test statistics and interim patients. The algorithm is described verbally, graphically and formally to facilitate immediate implementation in computer software. The optimized design exploits the aspired significance level better and is more powerful than the initial one. The algorithm applies to fixed sample and planned flexible adaptive designs for single- and multi-arm trials with one or multiple primary endpoints. The benefit increases with the number of interim patients. When planning a trial with interim analyses, the rules for decisions must be adjusted to interim patients. Otherwise, the test procedure is conservative resulting in loss of power. This is essential in situations where the number of interim patients is important compared to the first stage, particularly in trials with multiple primary endpoints.

Flexible sequential designs for multi-arm clinical trials.

Adaptive designs that are based on group-sequential approaches have the benefit of being efficient as stopping boundaries can be found that lead to good operating characteristics with test decisions based solely on sufficient statistics. The drawback of these so called 'pre-planned adaptive' designs is that unexpected design changes are not possible without impacting the error rates. 'Flexible adaptive designs' on the other hand can cope with a large number of contingencies at the cost of reduced efficiency. In this work, we focus on two different approaches for multi-arm multi-stage trials, which are based on group-sequential ideas, and discuss how these 'pre-planned adaptive designs' can be modified to allow for flexibility. We then show how the added flexibility can be used for treatment selection and sample size reassessment and evaluate the impact on the error rates in a simulation study. The results show that an impressive overall procedure can be found by combining a well chosen pre-planned design with an application of the conditional error principle to allow flexible treatment selection.

Treatment selection in multi-arm, multi-stage clinical trials

Adaptive designs that are based on group-sequential approaches have the benefit of being efficient as stopping boundaries can be found that lead to good operating characteristics with test decisions based solely on sufficient statistics. The drawback of these so called “pre-planned adaptive” designs is that unexpected design changes are not possible without impacting the error rates. “Flexible adaptive designs”, and in particular designs based on p-value combination, on the other hand can cope with a large number of contingencies at the cost of reduced efficiency. In this presentation we focus on so called multi-arm multi-stage trials which compare several active treatments against control at a series of interim analysis. We will discuss how these “pre-planned adaptive designs” can be modified to allow for flexibility. We then show how the added flexibility can be used for treatment selection and evaluate the impact on power in a simulation study. The results show that a combination of a well chosen pre-planned design and an application of the conditional error principle to allow flexible treatment selection results in an impressive overall procedure.

On Flexibility of Adaptive Designs and Criteria for Choosing A Good One—A Discussion of FDA Draft Guidance

In recent years, the use of adaptive design methods in clinical trials has attracted much attention due to its flexibility in identifying the best clinical benefit of the test treatment under investigation. The flexibility, however, comes at the price of decreasing the accuracy and reliability of the statistical inference drawn. In addition, it is susceptible to abuse. The Food and Drug Administration (FDA) draft guidance justifiably distinguishes between well-understood and less well-understood adaptive designs and suggests the use of the latter with caution. In this discussion paper, we further classify the less well-understood adaptive designs into the categories of flexible and wildly flexible ones and recommend the latter not be used. In addition, we suggest a set of performance characteristics as criteria for choosing a good design from a pool of flexible adaptive designs and group sequential designs.