Decision-Making in Drug Development: Application of a Model Based Framework for Assessing Trial Performance

2010 ◽  
pp. 61-83 ◽  
Author(s):  
Mike K. Smith ◽  
Jonathan L. French ◽  
Kenneth G. Kowalski ◽  
Matthew M. Hutmacher ◽  
Wayne Ewy
Keyword(s):  
Decision Making ◽  
Drug Development ◽  
Model Based ◽  
Development Application ◽  
Trial Performance

Model-Based Drug Development Survey Finds Pharmacometrics Impacting Decision Making in the Pharmaceutical Industry

2010 ◽  
Vol 50 (S9) ◽  
pp. 20S-30S ◽  
Author(s):  
Julie A. Stone ◽  
Christopher Banfield ◽  
Marc Pfister ◽  
Stacey Tannenbaum ◽  
Sandy Allerheiligen ◽  
...  
Keyword(s):  
Decision Making ◽  
Pharmaceutical Industry ◽  
Drug Development ◽  
Model Based ◽  
Model Based Drug Development

scholarly journals Bayesian modeling and simulation to inform rare disease drug development early decision-making: application to Duchenne muscular dystrophy

2021 ◽  
Author(s):  
Janelle L. Lennie ◽  
John T. Mondick ◽  
Marc R. Gastonguay
Keyword(s):  
Decision Making ◽  
Natural History ◽  
Drug Development ◽  
Rare Disease ◽  
Bayesian Model ◽  
Short Term ◽  
Decision Accuracy ◽  
Model Based ◽  
Term Trial ◽  
Frequentist Method

AbstractRare disease clinical trials are constrained to small sample sizes and may lack placebo-control, leading to challenges in drug development. This paper proposes a Bayesian model-based framework for early go/no-go decision making in rare disease drug development, using Duchenne muscular dystrophy (DMD) as an example. Early go/no-go decisions were based on projections of long-term functional outcomes from a Bayesian model-based analysis of short-term trial data informed by prior knowledge based on 6MWT natural history literature data in DMD patients. Frequentist hypothesis tests were also applied as a reference analysis method. A number of combinations of hypothetical trial designs, drug effects and cohort comparison methods were assessed.The proposed Bayesian model-based framework was superior to the frequentist method for making go/no-go decisions across all trial designs and cohort comparison methods in DMD. The average decision accuracy rates across all trial designs for the Bayesian and frequentist analysis methods were 45.8 and 8.98%, respectively. A decision accuracy rate of at least 50% was achieved for 42 and 7% of the trial designs under the Bayesian and frequentist analysis methods, respectively. The frequentist method was limited to the short-term trial data only, while the Bayesian methods were informed with both the short-term data and prior information. The specific results of the DMD case study were limited due to incomplete specification of individual-specific covariates in the natural history literature data and should be reevaluated using a full natural history dataset. These limitations aside, the framework presented provides a proof of concept for the utility of Bayesian model-based methods for decision making in rare disease trials.

Decision-Making in Drug Development: Application of a Clinical Utility IndexSM

2010 ◽  
pp. 85-107 ◽  
Author(s):  
Timothy J. Carrothers ◽  
F. Lee Hodge ◽  
Robert J. Korsan ◽  
William B. Poland ◽  
Kevin H. Dykstra
Keyword(s):  
Decision Making ◽  
Drug Development ◽  
Clinical Utility ◽  
Development Application

Integrating dose estimation into a decision-making framework for model-based drug development

2018 ◽  
Vol 17 (2) ◽  
pp. 155-168 ◽  
Author(s):  
James Dunyak ◽  
Patrick Mitchell ◽  
Bengt Hamrén ◽  
Gabriel Helmlinger ◽  
James Matcham ◽  
...  
Keyword(s):  
Decision Making ◽  
Drug Development ◽  
Dose Estimation ◽  
Model Based ◽  
Model Based Drug Development ◽  
Decision Making Framework

scholarly journals Extended VIKOR-QUALIFLEX Method Based on Trapezoidal Fuzzy Two-Dimensional Linguistic Information for Multiple Attribute Decision-Making with Unknown Attribute Weight

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 37
Author(s):  
Ye Li ◽  
Yisheng Liu
Keyword(s):  
Decision Making ◽  
Qualitative Evaluation ◽  
Multiple Attribute Decision Making ◽  
Two Dimensional ◽  
Linguistic Variables ◽  
Linguistic Information ◽  
Attribute Weight ◽  
Model Based ◽  
Subjective Weight ◽  
Multiple Attribute

Considering the advantages of trapezoid fuzzy two-dimensional linguistic variables (TrF2DLVs), which can not only accurately describe the qualitative evaluation but also use qualitative linguistic variables (LVs) to describe the confidence level of this evaluation in the second dimension, this paper proposes a novel method based on trapezoidal fuzzy two-dimensional linguistic information to solve multiple attribute decision-making (MADM) problems with unknown attribute weight. First, a combination weight model is constructed, which covers a subjective weight determination model based on the proposed trapezoidal fuzzy two-dimensional linguistic best-worst method (TrF2DL-BWM) and an objective weight determination model based on the proposed CRITIC method. Then, in order to accurately rank the alternatives, an extended VIKOR-QUALIFLEX method is proposed, which can measure the concordance index of each ranking combination by means of group utility and individual maximum regret value of each evaluation alternative. Finally, a practical problem of lean management assessment for industrial residential projects is solved by the proposed method, and the effectiveness and advantages of the method are demonstrated by comparative analysis and discussion.

scholarly journals Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

2021 ◽  
Vol 13 (11) ◽  
pp. 6038
Author(s):  
Sergio Alonso ◽  
Rosana Montes ◽  
Daniel Molina ◽  
Iván Palomares ◽  
Eugenio Martínez-Cámara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Sustainable Development Goals ◽  
Modern Society ◽  
Agenda 2030 ◽  
Model Based ◽  
Development Goals ◽  
Sustainability Challenges ◽  
New Research ◽  
Decision Making Model

The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.

Sign in / Sign up

Export Citation Format

Share Document