Optimal Design of Dose Response Experiments: A Model-Oriented Approach

Fedorov, Valerii V.; Leonov, Sergei

doi:10.1177/009286150103500433

Cited by 25 publications

(18 citation statements)

References 32 publications

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“…The Emax-sigmoid model and the 4-parameter logistic model have been frequently used for concentration-response or dose-response modeling. [27][28][29][30][31] The absolute difference between the best 4PL approximation and these dose response curves is less than 0.1 at all doses. The parametrization we use for 4PL is:…”

Section: Analysis Models For Phase 2 Efficacy Datamentioning

confidence: 97%

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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Section: Analysis Models For Phase 2 Efficacy Datamentioning

confidence: 97%

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

et al. 2012

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“…In addition to (number of) dose levels, other design factors can be optimized to ensure collection of informative data in exploratory trials, such as number of patients (study size), number of samples, sampling times, and study duration. [63][64][65][66][67][68][69] A major challenge in optimal design of nonlinear models is that the FIM is a function of unknown parameters. Such parameter estimates can sometimes be obtained from previous studies or another relevant study i.e., another drug in the class.…”

Section: Multiple Comparison Procedures and Modeling (Mcp-mod)mentioning

confidence: 99%

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4–5 December 2014)

Musuamba

Manolis²,

Holford

et al. 2017

CPT Pharmacom & Syst Pharma

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Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

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“…Although the consideration of optimal designs for dose response models has found considerable interest in the recent literature [see for example Fedorov and Leonov (2001), Wang (2006), Zhu and Wong (2000), Dette et al (2008), Dragalin et al (2010) and Dette et al (2013) among many others], we are not aware of any work on design of experiments for the comparison of two parametric regression curves. However, the effective planning of the experiments in the comparison of curves will yield to a substantially more accurate statistical inference.…”

Section: Introductionmentioning

confidence: 99%

Optimal designs for comparing curves

Dette¹,

Schorning²

2016

Ann. Statist.

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We consider the optimal design problem for a comparison of two regression curves, which is used to establish the similarity between the dose response relationships of two groups. An optimal pair of designs minimizes the width of the confidence band for the difference between the two regression functions. Optimal design theory (equivalence theorems, efficiency bounds) is developed for this non standard design problem and for some commonly used dose response models optimal designs are found explicitly. The results are illustrated in several examples modeling dose response relationships. It is demonstrated that the optimal pair of designs for the comparison of the regression curves is not the pair of the optimal designs for the individual models. In particular it is shown that the use of the optimal designs proposed in this paper instead of commonly used “non-optimal” designs yields a reduction of the width of the confidence band by more than 50%.

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Optimal Design of Dose Response Experiments: A Model-Oriented Approach

Cited by 25 publications

References 32 publications

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

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

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4–5 December 2014)

Optimal designs for comparing curves

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