25 years of Bayesian methods in the pharmaceutical industry: a personal, statistical bummel

Grieve, Andy

doi:10.1002/pst.315

Cited by 18 publications

(22 citation statements)

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“…Spiegelhalter provides many excellent case studies, with details about interventions used, study aims, design, results, computational details, Bayesian interpretation, sensitivity analysis, and general comments – in short, very useful examples of the sort badly needed. A few articles have suggested templates for examples involving Bayesian applications in clinical trials . In a similar vein, the FDA Bayesian guidance for device trials provides suggestions for the information needed in the submission of protocols involving Bayesian analyses.…”

Section: Bayesian Surveymentioning

confidence: 99%

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The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group

Natanegara

Neuenschwander

Seaman

et al. 2013

Pharmaceutical Statistics

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Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability.

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Section: Bayesian Surveymentioning

confidence: 99%

“…We now summarize our results along these lines. [6,7,9,10]. In a similar vein, the FDA Bayesian guidance for device trials [16] provides suggestions for the information needed in the submission of protocols involving Bayesian analyses.…”

Section: Summary Of Dia Bayesian Surveymentioning

confidence: 99%

The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group

Natanegara

Neuenschwander

Seaman

et al. 2013

Pharmaceutical Statistics

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“…There are two publications about a decade ago that help to set that stage, with extensive reviews of Bayesian methods, one in biostatistics by (Ashby, 2006) and the other in biopharmaceuticals by (Grieve, 2007). In particular, with little personal experience on toxicity monitoring (Phase I drug trials), dose finding (Phase II drug trials or on pharmacokinetic or pharmacodynamic studies for drugs), or postmarket safety, any review of these topics will not be attempted here.…”

Section: Introductionmentioning

confidence: 99%

Bayesian methods in clinical trials with applications to medical devices

Campbell

2017

CSAM

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Bayesian statistics can play a key role in the design and analysis of clinical trials and this has been demonstrated for medical device trials. By 1995 Bayesian statistics had been well developed and the revolution in computing powers and Markov chain Monte Carlo development made calculation of posterior distributions within computational reach. The Food and Drug Administration (FDA) initiative of Bayesian statistics in medical device clinical trials, which began almost 20 years ago, is reviewed in detail along with some of the key decisions that were made along the way. Both Bayesian hierarchical modeling using data from previous studies and Bayesian adaptive designs, usually with a non-informative prior, are discussed. The leveraging of prior study data has been accomplished through Bayesian hierarchical modeling. An enormous advantage of Bayesian adaptive designs is achieved when it is accompanied by modeling of the primary endpoint to produce the predictive posterior distribution. Simulations are crucial to providing the operating characteristics of the Bayesian design, especially for a complex adaptive design. The 2010 FDA Bayesian guidance for medical device trials addressed both approaches as well as exchangeability, Type I error, and sample size. Treatment response adaptive randomization using the famous extracorporeal membrane oxygenation example is discussed. An interesting real example of a Bayesian analysis using a failed trial with an interesting subgroup as prior information is presented. The implications of the likelihood principle are considered. A recent exciting area using Bayesian hierarchical modeling has been the pediatric extrapolation using adult data in clinical trials. Historical control information from previous trials is an underused area that lends itself easily to Bayesian methods. The future including recent trends, decision theoretic trials, Bayesian benefit-risk, virtual patients, and the appalling lack of penetration of Bayesian clinical trials in the medical literature are discussed.

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“…However, the Bayesian approach or the mixed Bayesianfrequentist approach has started to garner considerable interest among the researchers. [4][5][6][7][8][9][10] In particular, the mixed Bayesianfrequentist is regarded as a favorable approach as it enables the investigators to use a proper prior at the designing stage and then switches to a weak prior in the final analysis. This then fulfills the regulatory requirement to have the data speaking for themselves.…”

Section: 10 Introductionmentioning

confidence: 99%

Determination of the Required Sample Size with Assurance for Three-Arm Non-Inferiority Trials

2013

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The concept of assurance in the two-arm non-inferiority trials has been explored, expressing the noninferiority margin as a clinically meaningful treatment difference. This short paper focuses on developing an assurance formula in the three-arm non-inferiority trial, based on the ratio of means. The discussion starts with the simple case of known variances and then extends to the case of unknown but equal variances. To avoid complicated integration, assurance for the latter case was studied using Bayesian Clinical Trial Simulation (BCTS). The findings indicate that assurance allows the experimenter to formally take into account the uncertainty surrounding the parameter estimates by using the prior distributions. Furthermore, BCTS can be easily implemented to find the required sample size without having to resort to complex integration.Keywords: Assurance; power; non-inferiority trial; three-arm design; BCTS AbstrakKajian kaedah jaminan di dalam ujian tidak-inferior dua-kumpulan telah pun dibuat dengan mewakilkan margin tidak-inferior sebagai perbezaan rawatan yang bermakna (dalam konteks klinikal). Fokus kertas ini pula adalah terhadap pembentukan formula jaminan di dalam ujian tidak-inferior tiga-kumpulan, yang mewakilkan margin tidak-inferior sebagai nisbah min. Perbincangan dimulakan dengan kes asas di mana varians populasi dianggap diketahui dan dilanjutkan pada kes varians populasi tidak diketahui. Oleh kerana kes yang kedua ini melibatkan kamiran kompleks, kaedah jaminan telah diaplikasikan dengan bantuan Bayesian Clinical Trial Simulation (BCTS). Hasil kajian mendapati bahawa kaedah jaminan membolehkan penyelidik mengambil kira secara formal ketidakpastian berkenaan anggaran parameter yang terlibat di dalam pencarian saiz sample, iaitu dengan mewakilkan ketidakpastian tersebut menggunakan taburan prior yang bersesuaian. Selain itu, BCTS boleh diaplikasikan dengan mudah untuk mencari saiz sampel yang diperlukan tanpa perlu menyelesaikan masalah kamiran yang kompleks.Kata kunci: Jaminan; kuasa; ujian tidak-inferior; rekabentuk 3-kumpulan, BCTS

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25 years of Bayesian methods in the pharmaceutical industry: a personal, statistical bummel

Abstract: Twenty-five years ago the use of Bayesian methods in Pharmaceutical R&D was non-existent. Today that is no longer true. In this paper I describe my own personal journey along the road of discovery of Bayesian methods to routine use in the pharmaceutical industry.

Cited by 18 publications

References 94 publications

The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group

The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group

Bayesian methods in clinical trials with applications to medical devices

Determination of the Required Sample Size with Assurance for Three-Arm Non-Inferiority Trials

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