超越成功概率:统计学家在投资组合层面影响量化决策的机会。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-05-01 Epub Date: 2024-01-11 DOI:10.1002/pst.2361
Stig-Johan Wiklund, Katharine Thorn, Heiko Götte, Kimberley Hacquoil, Gaëlle Saint-Hilary, Alex Carlton
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引用次数: 0

摘要

制药业的开发周期长、成本高、风险大。因此,公司对项目组合的有效管理和优化是成功的关键。成功概率等项目指标可以为公司的研发项目建模,并考虑到该行业固有的高度不确定性。做出项目组合决策本身就涉及风险管理,而统计学家的理想定位是不仅支持单个项目的指标推导,而且倡导在更广泛的项目组合层面做出决策。本文旨在研究现有的不同投资组合决策方法,并提出统计人员在引入概率思维、定量决策和日益先进的方法方面的增值机会。
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Going beyond probability of success: Opportunities for statisticians to influence quantitative decision-making at the portfolio level.

The pharmaceutical industry is plagued with long, costly development and high risk. Therefore, a company's effective management and optimisation of a portfolio of projects is critical for success. Project metrics such as the probability of success enable modelling of a company's pipeline accounting for the high uncertainty inherent within the industry. Making portfolio decisions inherently involves managing risk, and statisticians are ideally positioned to champion not only the derivation of metrics for individual projects, but also advocate decision-making at a broader portfolio level. This article aims to examine the existing different portfolio decision-making approaches and to suggest opportunities for statisticians to add value in terms of introducing probabilistic thinking, quantitative decision-making, and increasingly advanced methodologies.

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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
期刊最新文献
Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes. Prediction Intervals for Overdispersed Poisson Data and Their Application in Medical and Pre-Clinical Quality Control. Treatment Effect Measures Under Nonproportional Hazards. Bayesian Response Adaptive Randomization for Randomized Clinical Trials With Continuous Outcomes: The Role of Covariate Adjustment. PKBOIN-12: A Bayesian Optimal Interval Phase I/II Design Incorporating Pharmacokinetics Outcomes to Find the Optimal Biological Dose.
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