Dynamically Optimizing Budget Allocation for Phase 3 Drug Development Portfolios Incorporating Uncertainty in the Pipeline

Patel, Nitin R.; Ankolekar, Suresh

doi:10.1007/978-3-319-09075-7_11

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…They calculated the probability of success of a trial by combining frequentist and Bayesian approaches assuming that a prior distribution is known for ecacy. The decision variables are binary, indicating whether a trial for a drug is started with a certain type of design at a particular time t. They also developed a stochastic integer model (Patel and Ankolekar, 2015) that provides solutions for possible Phase 3 outcomes. Colvin and Maravelias (2008) developed a multi-stage stochastic programming model to nd the trials to be performed for a given portfolio for each planning period.…”

Section: Mathematical Programming-based Approachesmentioning

confidence: 99%

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Pharmaceutical R & D pipeline management under trial duration uncertainty

Gokalp

Branke

2020

Computers & Chemical Engineering

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Section: Mathematical Programming-based Approachesmentioning

confidence: 99%

“…et al (2008) Colvin and Maravelias (2008), (2010) Solak et al (2010) Perez-Escobedo et al (2012)Patel and Ankolekar (2015) …”

mentioning

confidence: 99%

Pharmaceutical R & D pipeline management under trial duration uncertainty

Gokalp

Branke

2020

Computers & Chemical Engineering

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“…Traditional methods for portfolio decision making often focus on different ranking algorithms, which can be improved upon by the use of mathematical optimization [4]. For an optimal project selection, mathematical modeling and optimization techniques can improve budget allocation to the right projects to achieve a maximal performance of the portfolio.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

Farid¹,

Hallman²,

Palmblad³

et al. 2021

Mathematics

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This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

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Friction and Decision Rules in Portfolio Decision Analysis

Summers¹

2021

Decision Analysis

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In portfolio decision analysis, features comprise the objectives, alternatives, physics, and information that define a decision context. By modeling features, decision analysts forecast the expected utilities of the alternatives. A model is complete if it contains all the features. A model is well-calibrated if it correctly predicts the probability distributions of each alternative’s utility, whereas ill-calibrated models, like those that suffer the optimizer’s curse, do not. Friction identifies qualities of a situation that prevent decision analysts from creating complete, well-calibrated models. When friction is significant, can maximizing expected utility be a suboptimal decision rule? Is satisfying decision theory’s axioms a necessary or sufficient condition for good decision making? Can rules that violate the axioms outperform rules that satisfy them? A simulation study of how unbiased, imprecise forecasts of payoffs affect project selection finds that, for the example tested, the answers are yes, no, and yes, which suggests that further studies of friction may be worthwhile. Discussions of friction bookend the study, starting the paper by defining friction and concluding by presenting three frameworks, each one from a different field of study, that provide mathematical tools for studying friction.

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Dynamically Optimizing Budget Allocation for Phase 3 Drug Development Portfolios Incorporating Uncertainty in the Pipeline

Cited by 3 publications

References 9 publications

Pharmaceutical R & D pipeline management under trial duration uncertainty

Pharmaceutical R & D pipeline management under trial duration uncertainty

Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

Friction and Decision Rules in Portfolio Decision Analysis

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