Designing Phase 2 Trials Based on Program-Level Considerations: A Case Study for Neuropathic Pain
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Designing Phase 2 Trials Based on Program-Level Considerations: A Case Study for Neuropathic Pain
Drug Information Journal 46(4) 439-454 ª The Author(s) 2012 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0092861512444031 http://dij.sagepub.com
Nitin Patel, PhD1, James Bolognese, MStat1, Christy Chuang-Stein, PhD2, David Hewitt, MD3, Arnold Gammaitoni, PharmD4, and Jose Pinheiro, PhD5
Abstract Traditionally, sample size considerations for phase 2 trials are based on the desired properties of the design and response information from the trials. In this article, we propose to design phase 2 trials based on program-level optimization. We present a framework to evaluate the impact that several phase 2 design features have on the probability of phase 3 success and the expected net present value of the product. These factors include the phase 2 sample size, decision rules to select a dose for phase 3 trials, and the sample size for phase 3 trials. Using neuropathic pain as an example, we use simulations to illustrate the framework and show the benefit of including these factors in the overall decision process. Keywords maximum utility dose selection method, net present value, probability of success, target efficacy dose selection method
1. Background Traditionally, product development is divided into distinct phases and planning is carried out for each phase separately. When determining the sample size for a trial, most clinical trialists focus on statistical power or the desired precision in estimation, although some researchers have advocated for cost-effective trial designs.1-3 Recently, researchers have paid increasing attention to the concept of assurance (probability of success). They state that the assurance should be at an acceptable level when planning a confirmatory trial.4-7 There is also some published work on examining development strategies beyond a single trial and more quantitative approaches to making go/no go decisions between phases.8-14 These efforts have raised questions of how to optimize study design in the context of a development program and how to choose appropriate criteria for optimization. Concerns over products’ effective patent life have led many product developers to place a premium on speed. A longer remaining patent life at the point of product launch will translate to higher net revenue for the developer. Patel and Ankolekar proposed to incorporate economic factors in deciding sample size and design for clinical trials as well as assessing portfolios of drugs.15 Burman et al used a decision-analytic approach to calculate sample size from a perspective of
maximizing company profits.9 Mehta and Patel use net revenue and net present value (NPV) as factors when considering sample size reestimation in a confirmatory trial.16 Similar ideas related to NPV are also part of the assessment criteria.9,11,12 Despite such efforts, NPV has rarely been formally incorporated into decisions on design strategy. In this article, we expand the NPV concept in Mehta and Patel1
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