scholarly journals Adaptive Clinical Trials: Advantages and Disadvantages of Various Adaptive Design Elements

Author(s):  
Edward L. Korn ◽  
Boris Freidlin
2019 ◽  
pp. 1-12
Author(s):  
Chen Hu ◽  
James J. Dignam

In this precision oncology era, where molecular profiling at the individual patient level becomes increasingly accessible and affordable, more and more clinical trials are now driven by biomarkers, with an overarching objective to optimize and personalize disease management. As compared with the conventional clinical development paradigms, where the key is to evaluate treatment effects in histology-defined populations, the choices of biomarker-driven clinical trial designs and analysis plans require additional considerations that are heavily dependent on the nature of biomarkers (eg, prognostic or predictive, integral or integrated) and the credential of biomarkers’ performance and clinical utility. Most recently, another major paradigm change in biomarker-driven trials is to conduct multi-agent and/or multihistology master protocols or platform trials. These trials, although they may enjoy substantial infrastructure and logistical advantages, also face unique operational and conduct challenges. Here we provide a concise overview of design options for both the setting of single-biomarker/single-disease and the setting of multiple-biomarker/multiple-disease types. We focus on explaining the trial design and practical considerations and rationale of when to use which designs, as well as how to incorporate various adaptive design components to provide additional flexibility, enhance logistical efficiency, and optimize resource allocation. Lessons learned from real trials are also presented for illustration.


Author(s):  
Alessandra Giovagnoli

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.


2015 ◽  
Vol 49 (1) ◽  
pp. 100-107 ◽  
Author(s):  
Nancy Burnham ◽  
Judith Quinlan ◽  
Weili He ◽  
Micheline Marshall ◽  
Graham Nicholls ◽  
...  

2017 ◽  
Vol 14 (3) ◽  
pp. 246-254 ◽  
Author(s):  
Samkeliso C Mawocha ◽  
Michael D Fetters ◽  
Laurie J Legocki ◽  
Timothy C Guetterman ◽  
Shirley Frederiksen ◽  
...  

Background: Adaptive clinical trials use accumulating data from enrolled subjects to alter trial conduct in pre-specified ways based on quantitative decision rules. In this research, we sought to characterize the perspectives of key stakeholders during the development process of confirmatory-phase adaptive clinical trials within an emergency clinical trials network and to build a model to guide future development of adaptive clinical trials. Methods: We used an ethnographic, qualitative approach to evaluate key stakeholders’ views about the adaptive clinical trial development process. Stakeholders participated in a series of multidisciplinary meetings during the development of five adaptive clinical trials and completed a Strengths–Weaknesses–Opportunities–Threats questionnaire. In the analysis, we elucidated overarching themes across the stakeholders’ responses to develop a conceptual model. Results: Four major overarching themes emerged during the analysis of stakeholders’ responses to questioning: the perceived statistical complexity of adaptive clinical trials and the roles of collaboration, communication, and time during the development process. Frequent and open communication and collaboration were viewed by stakeholders as critical during the development process, as were the careful management of time and logistical issues related to the complexity of planning adaptive clinical trials. Conclusion: The Adaptive Design Development Model illustrates how statistical complexity, time, communication, and collaboration are moderating factors in the adaptive design development process. The intensity and iterative nature of this process underscores the need for funding mechanisms for the development of novel trial proposals in academic settings.


Author(s):  
Paula P. Schnurr ◽  
Jessica L. Hamblen

This chapter provides an overview of key concepts in designing and evaluating clinical trials, with a focus on randomized controlled trials for PTSD. The first section discusses design elements and how they influence the conclusions that can be drawn from a study. Examples from the trauma literature are provided when available to illustrate concepts. The second section explores newer developments in PTSD treatment trials. Specifically, it discusses treatment and design considerations related to common comorbid conditions of PTSD, adapting treatments for low-resource environments and optimizing treatment outcome. The chapter’s goal is to improve the ability of both clinicians and researchers to critically review PTSD clinical trials.


Author(s):  
Alessandro Baldi Antognini ◽  
Marco Novelli ◽  
Maroussa Zagoraiou

AbstractThe present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.


2018 ◽  
Vol Volume 10 ◽  
pp. 343-351 ◽  
Author(s):  
Jay JH Park ◽  
Kristian Thorlund ◽  
Edward J Mills

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