A Bayesian Approach for Benefit-Risk Assessment

Zhao, Yueqin; Zalkikar, Jyoti; Tiwari, Ram C.; LaVange, Lisa M.

doi:10.1080/19466315.2014.965845

Cited by 16 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a longitudinal BR assessment, Zhao et al propose a Bayesian model to sequentially update information accrued across visits. 22 The model is based on Chuang-Stein’s BR response categories and measures. 23 It assumes that the subject-level outcomes of a clinical trial can be classified into 5 mutually exclusive categories: benefit without adverse effect, benefit with adverse effect, no benefit with no adverse effect, no benefit but with adverse effect, and withdrawal.…”

Section: Overview Of Bayesian Applications In Benefit-risk Evaluationsmentioning

confidence: 99%

“…Although the model in Zhao et al is based on Chuang-Stein’s 5 BR categories, it can be easily extended to include additional categories as needed with minimal adjustments to the Bayesian approach outlined. 22 Cui et al extend the methodology to derive subject-level probabilities of the different BR measures. 24…”

Section: Overview Of Bayesian Applications In Benefit-risk Evaluationsmentioning

confidence: 99%

See 1 more Smart Citation

The Case for a Bayesian Approach to Benefit-Risk Assessment: Overview and Future Directions

Costa

Jemiai

et al. 2017

Ther Innov Regul Sci

Self Cite

View full text Add to dashboard Cite

The benefit-risk assessment of a new medicinal product or intervention is one of the most complex tasks that sponsors, regulators, payers, physicians, and patients face. Therefore, communicating the trade-off of benefits and risks in a clear and transparent manner, using all available evidence, is critical to ensure that the best decisions are made. Several quantitative methods have been proposed in recent years that try to provide insight into this challenging problem. Bayesian inference, with its coherent approach for integrating different sources of information and uncertainty, along with its links to optimal decision theory, provides a natural framework to perform quantitative assessments of the benefit-risk trade-off. This paper describes the current state of the art in Bayesian methodologies for quantitative benefit-risk assessment, and how these may be leveraged throughout the life cycle of a medicinal product to support and augment clinical judgment and qualitative benefit-risk assessments. Gaps and potential new directions that extend the current approaches are also identified.

show abstract

Section: Overview Of Bayesian Applications In Benefit-risk Evaluationsmentioning

confidence: 99%

Section: Overview Of Bayesian Applications In Benefit-risk Evaluationsmentioning

confidence: 99%

The Case for a Bayesian Approach to Benefit-Risk Assessment: Overview and Future Directions

Costa

Jemiai

et al. 2017

Ther Innov Regul Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…Usually, the ratio measurements are evaluated at the log scale, which are log (BR-R) and log (BR-CR). In this article, we adopt the choice of ω j as given in Chuang-Stein et al [4] and Zhao et al [15] that is, we let (ω 1 , ω 2 , ω 3 , ω 4 , ω 5 ) = (2, 1, 0, 1, 2), considering benefit with no AEs has a higher weight than benefit with AEs in terms of benefit; and withdrawal has a higher weight than no benefit with AE in terms of risk. We also assume e = h = 1 for simplicity.…”

Section: Benefit and Risk Categories And Measuresmentioning

confidence: 99%

Bayesian Approach to Personalized Benefit-Risk Assessment

Cui

Zhao

Tiwari

2016

Statistics in Biopharmaceutical Research

Self Cite

View full text Add to dashboard Cite

Benefit-risk assessment is critical in evaluating the effectiveness of a new drug before and after the approval. Some benefit-risk measures depend on the probabilities of benefit-risk categories in which the subject-level benefit and risk outcomes are characterized. The existing benefit-risk methods for analyzing the categorical data depend only on the frequencies of mutually exclusive and collectively exhaustive categories that the subjects fall in, and thus ignore the subject-level differences. We propose a Bayesian method for analyzing the subject-level data with multiple visits. A generalized linear model is used to model the subject-level response probability, with respect to a "reference" category, assuming a logit model with subject-level category effects and multiple visit effects. The random longitudinal visit effects are modeled by a multivariate normal distribution with zero means and first order autoregressive structured variance-covariance matrices. In the proposed Bayesian setup, a Dirichlet process is used as a prior for the subject-level category effect to catch the similarity among the subject responses. We develop an efficient Markov chain Monte Carlo algorithm for implementing the proposed method, and illustrate the estimation of individual benefit-risk profiles through simulation. The performance of the proposed model fit is evaluated using two model selection approaches, namely the deviance information criterion (DIC) and the log-pseudo marginal * Shiqi Cui (scrr2@mail.missouri.edu) is Graduate Student, ACCEPTED MANUSCRIPTlikelihood (LPML). We analyze a clinical trial data using the proposed method to assess the subject-level or personalized benefit-risk in each arm, and to evaluate the aggregated benefitrisk difference between the treatments at different visits.

show abstract

“…Recent efforts have attempted to move beyond the structuring step to a more quantitative framework that allows sponsors and regulators to gain further insight into specific aspects of a drug's BR profile. Techniques such as multicriteria decision analysis (MCDA), decision contours, or weighted BR scores have proved useful tools for quantitative BR assessments . A comprehensive review of quantitative approaches for BR evaluations can be found in Mt‐Isa et al…”

Section: Introductionmentioning

confidence: 99%

“…Techniques such as multicriteria decision analysis (MCDA), decision contours, or weighted BR scores have proved useful tools for quantitative BR assessments. [4][5][6][7] A comprehensive review of quantitative approaches for BR evaluations can be found in Mt-Isa et al 8 An additional challenge of any BR assessment is the fact that exposure to an investigational drug creates the potential for efficacy and safety outcomes to be connected at the subject level. In this scenario, analysing efficacy and safety responses separately could lead to misleading results.…”

Section: Introductionmentioning

confidence: 99%

Bayesian joint modelling of benefit and risk in drug development

Costa

Drury²

2018

Pharmaceutical Statistics

View full text Add to dashboard Cite

To gain regulatory approval, a new medicine must demonstrate that its benefits outweigh any potential risks, ie, that the benefit-risk balance is favourable towards the new medicine. For transparency and clarity of the decision, a structured and consistent approach to benefit-risk assessment that quantifies uncertainties and accounts for underlying dependencies is desirable. This paper proposes two approaches to benefit-risk evaluation, both based on the idea of joint modelling of mixed outcomes that are potentially dependent at the subject level. Using Bayesian inference, the two approaches offer interpretability and efficiency to enhance qualitative frameworks. Simulation studies show that accounting for correlation leads to a more accurate assessment of the strength of evidence to support benefit-risk profiles of interest. Several graphical approaches are proposed that can be used to communicate the benefit-risk balance to project teams. Finally, the two approaches are illustrated in a case study using real clinical trial data.

show abstract

A Bayesian Approach for Benefit-Risk Assessment

Cited by 16 publications

References 18 publications

The Case for a Bayesian Approach to Benefit-Risk Assessment: Overview and Future Directions

The Case for a Bayesian Approach to Benefit-Risk Assessment: Overview and Future Directions

Bayesian Approach to Personalized Benefit-Risk Assessment

Bayesian joint modelling of benefit and risk in drug development

Contact Info

Product

Resources

About