Enhancing R&amp;D in science-based industry: An optimal stopping model for drug discovery

Zhao, Guoguang; Chen, Wen

doi:10.1016/j.ijproman.2009.01.003

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…The opportunity cost can be thought of as the financial cost of delaying a decision to obtain more information. 32 Willan and Kowgier 33 suggested, for a 2-arm trial, the opportunity cost is equal to the incremental net benefit (INB) of the new intervention compared with the control based on information available before the trial begins.…”

Section: Methodsmentioning

confidence: 99%

Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials

et al. 2021

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Introduction Adaptive designs allow changes to an ongoing trial based on prespecified early examinations of accrued data. Opportunities are potentially being missed to incorporate health economic considerations into the design of these studies. Methods We describe how to estimate the expected value of sample information for group sequential design adaptive trials. We operationalize this approach in a hypothetical case study using data from a pilot trial. We report the expected value of sample information and expected net benefit of sampling results for 5 design options for the future full-scale trial including the fixed-sample-size design and the group sequential design using either the Pocock stopping rule or the O’Brien-Fleming stopping rule with 2 or 5 analyses. We considered 2 scenarios relating to 1) using the cost-effectiveness model with a traditional approach to the health economic analysis and 2) adjusting the cost-effectiveness analysis to incorporate the bias-adjusted maximum likelihood estimates of trial outcomes to account for the bias that can be generated in adaptive trials. Results The case study demonstrated that the methods developed could be successfully applied in practice. The results showed that the O’Brien-Fleming stopping rule with 2 analyses was the most efficient design with the highest expected net benefit of sampling in the case study. Conclusions Cost-effectiveness considerations are unavoidable in budget-constrained, publicly funded health care systems, and adaptive designs can provide an alternative to costly fixed-sample-size designs. We recommend that when planning a clinical trial, expected value of sample information methods be used to compare possible adaptive and nonadaptive trial designs, with appropriate adjustment, to help justify the choice of design characteristics and ensure the cost-effective use of research funding. Highlights Opportunities are potentially being missed to incorporate health economic considerations into the design of adaptive clinical trials. Existing expected value of sample information analysis methods can be extended to compare possible group sequential and nonadaptive trial designs when planning a clinical trial. We recommend that adjusted analyses be presented to control for the potential impact of the adaptive designs and to maintain the accuracy of the calculations. This approach can help to justify the choice of design characteristics and ensure the cost-effective use of limited research funding.

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Section: Methodsmentioning

confidence: 99%

Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials

et al. 2021

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“…Kwon (2010) studies the firm's decision to discard or invest in an aging technology with a declining profit stream with demand uncertainty. The theory of optimal stopping is applied to study project management with uncertain completion in Chi et al (1997), and to develop strategies for drug discovery in Zhao and Chen (2009), among others. Sequential stopping problems also arise in other applications, such as participating a government subsidized program (Huisman and Thijssen (2013)), and trading under mean reversion (Leung and Li (2015a,b)).…”

Section: Introductionmentioning

confidence: 99%

Timing Options for a Startup with Early Termination and Competition Risks

Leung

2016

SSRN Journal

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This paper analyzes the timing options embedded in a startup firm, and the associated market entry and exit timing decisions under the exogenous risks of early termination and competitor's entry. Our valuation approach leads to the analytical study of a non-standard perpetual American installment option nested with an optimal sequential stopping problem. Explicit formulas are derived for the firm's value functions. Analytically and numerically, we show that early termination risk leads to earlier voluntary entry or exit, and the threat of competition has a non-trivial effect on the firm's entry and abandonment strategies.

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“…[6] considered a two-dimensional dynamic model of an investment process. Zhao and Chen [7] investigated an optimal stopping model for pharmaceutical R&D projects, in which each individual round of lead compound arrives according to a Poisson process.…”

Section: Introductionmentioning

confidence: 99%

A Chance-Constrained Programming Model for the Optimal Stopping Decision of Power R&D Project

Guan

Gao

2013

AMR

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A power R&D process can be regarded as a jump process of scientific knowledge full of exploration and complexity. Every jump represents a scientific breakthrough or a new discovery in this setting. In this paper, the inter-arrival times are treated as random variables observe arbitrary distributions. The ɑ-optimistic net return of project performance is proposed and a chance-constrained programming model is established to model the power R&D optimal stopping decision problem. Considering the complexity of the model, the stochastic simulation is designed to estimate the values of project return performance and the simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to solve the proposed model. Finally, the effectiveness of the hybrid algorithm and the applicability of the model are illustrated by numerical examples.

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Enhancing R&D in science-based industry: An optimal stopping model for drug discovery

Cited by 10 publications

References 22 publications

Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials

Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials

Timing Options for a Startup with Early Termination and Competition Risks

A Chance-Constrained Programming Model for the Optimal Stopping Decision of Power R&D Project

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