A comparison of stochastic programming methods for portfolio level decision-making

Graham, Emily E.; Jaki, Thomas; Harbron, Chris

doi:10.1080/10543406.2019.1684307

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…Seamless designs [101,102,201], which we have not discussed in detail, use similar adaptive design methodology to combine phases of clinical development: designs may be inferentially seamless, where data from the earlier stage are incorporated into the overall trial results; operationally seamless, avoiding any break in recruitment between the stages of the development process but excluding data from earlier stages from the final analysis of the latter; or both. There are many motivations for conducting seamless designs [202], making it an active area of research [203].…”

Section: Discussionmentioning

confidence: 99%

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Burnett

Mozgunov

Pallmann

et al. 2020

BMC Med

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Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.

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Section: Discussionmentioning

confidence: 99%

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Burnett

Mozgunov

Pallmann

et al. 2020

BMC Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…Seamless designs [102,103,200], which we have not discussed in detail, use similar adaptive design methodology to combine phases of clinical development: designs may be inferentially seamless, where data from the earlier stage are incorporated into the overall trial results; operationally seamless, avoiding any break in recruitment between the stages of the development process but excluding data from earlier stages from the final analysis of the latter; or both. There are many motivations for conducting seamless designs [201], making it an active area of research [202]. For many years one major obstacle to the use of adaptive designs in practice has been the lack of suitable software to aid both the design and conduct of trials.…”

Section: Discussionmentioning

confidence: 99%

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Burnett¹,

Mozgunov²,

Pallmann³

et al. 2020

Preprint

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Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory Phase III studies.

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“…Much attention has been paid to the issue of how to select an optimal subset of available projects to fit within the available budget. [2][3][4][5][6][7][8][9] But drug development is not only costly, it is also highly uncertain. Given a success rate of only 10%-15% from the start of the clinical phase, 10 the most likely outcome of any drug project is a failure in one of the clinical phases.…”

Section: Introductionmentioning

confidence: 99%

“…Since drug development is exceptionally costly, with the cost per each successfully launched drug being hundreds of millions of dollars, 1 it is often the case that the budget allocated to R&D may not be sufficient to fund all the available projects for development. Much attention has been paid to the issue of how to select an optimal subset of available projects to fit within the available budget 2–9 . But drug development is not only costly, it is also highly uncertain.…”

Section: Introductionmentioning

confidence: 99%

“…Some authors have applied real option valuations as the basis for optimising pharmaceutical R&D portfolios, for example Reference 12. Graham et al 6 compared the method of real option valuation to a project scheduling approach based on the expected net present value when optimising portfolio decision‐making. A more simplistic quantitative approach would be to rank projects by ROI, select from the highest, and continue down the list until a constraint is reached 13…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Wiklund

Önnheim

Ytterstad

2023

Pharmaceutical Statistics

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Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this paper, we argue the need for a forward‐looking approach to portfolio decision‐making. We develop a quantitative model that allows the portfolio management to evaluate the need for future inflow of new projects to achieve revenue at desired levels, often aspiring to a certain annual revenue growth. Optimisation methods are developed for the presented model, allowing an optimal choice of number, timing and type of projects to be added to the portfolio. The proposed methodology allows for a proactive approach to portfolio management, prioritisation, and optimisation. It provides a quantitatively based support for strategic decisions regarding the efforts needed to secure the future development pipeline and revenue stream of the company.

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A comparison of stochastic programming methods for portfolio level decision-making

Cited by 5 publications

References 18 publications

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

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