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Sample sizes required to detect two-way and three-way interactions involving slope differences in mixed-effects linear models

M Heo , AC Leon
Journal of Biopharmaceutical Statistics. 2010 July;20
Based on maximum likelihood estimates obtained from mixed-effects linear models, closed-form power functions are derived to detect two-way and three-way interactions that involve longitudinal course of outcome over time in clinical trials. Sample size estimates are shown to decrease with increasing within-subject correlations. It is further shown that when clinical trial designs are balanced in group sizes, the sample size required to detect an effect size for a three-way interaction is exactly fourfold that required to detect the same effect size of a two-way interaction. Furthermore, this fourfold relationship virtually holds for unbalanced allocations of subjects if one factor is balanced in the three-way interaction model. Simulations are presented that verify the sample size estimates for two-way and three-way interactions.

An Introduction to Adaptive Designs and Adaptation in CNS Trials

V Dragalin
European Neuropsychopharmacology: February 2011 (Volume 21, Issue 2)
Adaptive designs learn from accumulating trial data in real time and apply this knowledge to optimize subsequent study execution. A set of design rules define a priori which modifications may be incorporated into the trial design. Judicious use of adaptive designs may increase the information value per resource unit invested by avoiding allocation of patients to non-efficacious/unsafe therapies and allowing stopping decisions to be made at the earliest possible time point. Ultimately this may accelerate the development of promising therapies.

Adaptive Clinical Trials for New Drug Applications in Japan

Y Ando , A Hirakawa, Y Uyama
European Neuropsychopharmacology Volume 21, Issue 2, February 2011
Adaptive design is regarded as an efficient method for clinical trials in order to increase the success rate of a new drug in development, and recently has been actively discussed among regulatory agencies, industry and academia. Since adaptive design involves interim analyses and is more complex than traditional fixed design, some points such as possibility of introducing statistical and operational bias should be considered when planning and implementing such trials. In this article, we share our perspectives in the consideration of adaptive design clinical trials based on our experiences discussing adaptive design in clinical trial consultation meetings in Japan.

Methodological Issues in Negative Symptom Trials

SR Marder , DG Daniel, L Alphs, AG Awad, RS Keefe
Schizophrenia Bulletin, 2011 Mar;37(2):250-4. Epub 2011 Jan 26
Individuals from academia, the pharmaceutical industry, and the US Food and Drug Administration used a workshop format to discuss important methodological issues in the design of trials of pharmacological agents for improving negative symptoms in schizophrenia. The issues addressed included the need for a coprimary functional measure for registration trials; the characteristics of individuals who should enter negative symptom trials; the optimal duration for a proof-of-concept or registration trial; the optimal design of a study of a broad-spectrum agent that treats both positive and negative symptoms or a co-medication that is added to an antipsychotic; the relative strengths and weaknesses of available instruments for measuring negative symptoms; the definition of clinically meaningful improvement for these trials; and whether drugs can be approved for a subdomain of negative symptoms.

Adaptive Clinical Trials: Overview of Early-Phase Designs and Challenges

O Marchenko , V Federoff, J Lee, C Nolan, J Pinheiro
Therapeutic Innovation and Regulatory Science: January 2014
In this paper, the authors describe developments in adaptive design methodology and discuss implementation strategies and operational challenges in early-phase adaptive clinical trials. The BATTLE trial—the first completed biomarker-based Bayesian adaptive randomized study in lung cancer—is presented as a case study to illustrate main ideas and share learnings.