Adaptive and Model-Based Dose-Ranging Trials: Quantitative Evaluation and Recommendations. White Paper of the PhRMA Working Group on Adaptive Dose-Ranging Studies

Pinheiro, José; Sax, Frederic L.; Antonijevic, Zoran; Bornkamp, Björn; Bretz, Frank; Chuang‐Stein, Christy; Dragalin, Vladimir; Fardipour, Parvin; Gallo, Paul; Gillespie, William R.; Hsu, Chyi‐Hung; Miller, Frank; Padmanabhan, S. Krishna; Patel, Nitin R.; Perevozskaya, Inna; Roy, Amit; Sanil, Ashish; Smith, Jonathan R.

doi:10.1198/sbr.2010.09054

Cited by 40 publications

(32 citation statements)

References 8 publications

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“…Since then the ADRS working group focused on investigating several further topics; see Pinheiro et al (2010) for the latest summary. A new workstream was created in 2008 with the goal to continue the ADRS working group investigation into novel designs and methods to improve dose finding in drug development.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Study to Compare New Adaptive Dose–Ranging Designs

Dragalin¹,

Bornkamp²,

Bretz³

et al. 2010

Statistics in Biopharmaceutical Research

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The main goals in an adaptive dose-ranging study are to detect dose response, to determine if any doses(s) meets clinical relevance, to estimate the dose-response, and then to decide on the dose(s) (if any) to take into the confirmatory Phase III. Adaptive dose-ranging study designs may result in power gains to detect dose response and higher precision in estimating the target dose and the dose response curve. In this article, we complement the library of available methods with five new adaptive doseranging designs. Due to their inherent complexity, the operating characteristics can be assessed only through intensive simulations. We present here results of a comprehensive simulation study that compares and contrasts these designs for a variety of different scenarios.

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Section: Introductionmentioning

confidence: 99%

A Simulation Study to Compare New Adaptive Dose–Ranging Designs

Dragalin¹,

Bornkamp²,

Bretz³

et al. 2010

Statistics in Biopharmaceutical Research

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“…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 Patel 16 to designing a phase 2 trial, choosing a dose selection method, and planning future phase 3 trials. We investigate how phase 2 design, go/no go decision, dose selection, and phase 3 design jointly impact the expected NPV (ENPV).…”

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confidence: 99%

Designing Phase 2 Trials Based on Program-Level Considerations: A Case Study for Neuropathic Pain

et al. 2012

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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 BackgroundTraditionally, 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 Patel 16 to designing a phase 2 trial, choosing a dose selection method, and planning future phase 3 trials. We investigate how phase 2 design, go/no go decision, dose selection, and phase 3 design jointly impact the expected NPV (ENPV). We also examine how these decisions together could affect the probability of success (PoS) of the confirmatory stage. The work reported here is part of a broad industry-academic Adaptive

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“…Recently there are several papers that use modeling to estimate the MED, see Bornkamp et al (2007) and Pinheiro et al (2010), among others. We followed the simulation setup in section 5 of Bretz et al (2005) to compare the MCP-Mod procedure with NEW as suggested by one referee and the Associate Editor.…”

Section: Simulation Studiesmentioning

confidence: 99%

A Step-Up Test Procedure to Find the Minimum Effective Dose

Wang¹,

Peng²

2014

Journal of Biopharmaceutical Statistics

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It is of great interest to find the minimum effective dose (MED) in dose-response studies. A sequence of decreasing null hypotheses to find the MED is formulated under the assumption of nondecreasing dose response means. A step-up multiple test procedure that controls the familywise error rate (FWER) is constructed based on the maximum likelihood estimators for the monotone normal means. When the MED is equal to one, the proposed test is uniformly more powerful than Hsu and Berger's test (1999). Also, a simulation study shows a substantial power improvement for the proposed test over four competitors. Three R-codes are provided in Supplemental Materials for this article. Go to the publishers online edition of Journal of Biopharmaceutical Statistics to view the files.

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Adaptive and Model-Based Dose-Ranging Trials: Quantitative Evaluation and Recommendations. White Paper of the PhRMA Working Group on Adaptive Dose-Ranging Studies

Cited by 40 publications

References 8 publications

A Simulation Study to Compare New Adaptive Dose–Ranging Designs

A Simulation Study to Compare New Adaptive Dose–Ranging Designs

Designing Phase 2 Trials Based on Program-Level Considerations: A Case Study for Neuropathic Pain

A Step-Up Test Procedure to Find the Minimum Effective Dose

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