Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

245
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
245
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

283
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
283
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

201
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
201
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

646
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
646

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same author

A randomized controlled Phase I de-escalation trial of molnupiravir and nirmatrelvir/ritonavir combination for mild-moderate SARS-CoV-2 infection.

The Journal of antimicrobial chemotherapy·2026
Same author

An evaluation of designs for Phase I/IIa dose-finding studies in Tuberculosis.

Statistical methods in medical research·2026
Same author

Will the Pharmaceutical Industry Need Statisticians in an AI World?

Pharmaceutical statistics·2026
Same author

The 'Hippocratic Oath' for AI-based clinical decision support systems.

BMC medical informatics and decision making·2026
Same author

Modern Clinical Trials: Seamless Designs and Master Protocols.

Cancer medicine·2026

Related Experiment Video

Updated: Jan 2, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.4K

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

Emily Graham1, Thomas Jaki1, Chris Harbron2

  • 1Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.

Journal of Biopharmaceutical Statistics
|December 12, 2019
PubMed
Summary

This study compares two methods for pharmaceutical portfolio management: real option valuation (ROV) and project scheduling (PS). The PS approach offers scheduling benefits but requires more computational resources than ROV.

Keywords:
Portfolio managementportfolio level decision makingproject schedulingreal option valuationresearch and development pipelinestochastic programming

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K

Related Experiment Videos

Last Updated: Jan 2, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.4K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K

Area of Science:

  • Pharmaceutical portfolio management
  • Operations research
  • Clinical trial optimization

Background:

  • Selecting clinical studies for pharmaceutical portfolios is complex.
  • Existing methods for portfolio management often use stochastic programming.
  • Two prominent approaches are real option valuation (ROV) and project scheduling (PS).

Purpose of the Study:

  • To compare the ROV and PS approaches for pharmaceutical portfolio selection.
  • To analyze the strengths and weaknesses of each method in managing clinical studies.
  • To evaluate the trade-offs between solution output and computational cost.

Main Methods:

  • Formulating portfolio management as a mixed integer linear programme (MILP).
  • Implementing the real option valuation (ROV) approach, valuing drug development programs stochastically.
  • Implementing the project scheduling (PS) approach, treating trial outcomes as stochastic components.

Main Results:

  • The ROV and PS approaches may yield different optimal portfolios due to differing stochastic components.
  • The PS approach provides an integrated schedule for clinical trials.
  • The PS approach incurs a significantly higher computational burden compared to ROV.

Conclusions:

  • Both ROV and PS are viable stochastic programming techniques for pharmaceutical portfolio management.
  • The choice between ROV and PS depends on whether integrated scheduling is prioritized over computational efficiency.
  • Further research could explore hybrid approaches to balance scheduling and computational demands.