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  • 1
    Online Resource
    Online Resource
    Wiley ; 2013
    In:  Pharmaceutical Statistics Vol. 12, No. 2 ( 2013-03), p. 104-113
    In: Pharmaceutical Statistics, Wiley, Vol. 12, No. 2 ( 2013-03), p. 104-113
    Abstract: To quantify uncertainty in a formal manner, statisticians play a vital role in identifying a prior distribution for a Bayesian‐designed clinical trial. However, when expert beliefs are to be used to form the prior, the literature is sparse on how feasible and how reliable it is to elicit beliefs from experts. For late‐stage clinical trials, high importance is placed on reliability; however, feasibility may be equally important in early‐stage trials. This article describes a case study to assess how feasible it is to conduct an elicitation session in a structured manner and to form a probability distribution that would be used in a hypothetical early‐stage trial. The case study revealed that by using a structured approach to planning, training and conduct, it is feasible to elicit expert beliefs and form a probability distribution in a timely manner. We argue that by further increasing the published accounts of elicitation of expert beliefs in drug development, there will be increased confidence in the feasibility of conducting elicitation sessions. Furthermore, this will lead to wider dissemination of the pertinent issues on how to quantify uncertainty to both practicing statisticians and others involved with designing trials in a Bayesian manner. Copyright © 2013 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 1539-1604 , 1539-1612
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2013
    detail.hit.zdb_id: 2083706-9
    detail.hit.zdb_id: 2163550-X
    SSG: 15,3
    Location Call Number Limitation Availability
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  • 2
    In: Pharmaceutical Statistics, Wiley, Vol. 13, No. 1 ( 2014-01), p. 3-12
    Abstract: 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. Copyright © 2013 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 1539-1604 , 1539-1612
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 2083706-9
    detail.hit.zdb_id: 2163550-X
    SSG: 15,3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    In: Pharmaceutical Statistics, Wiley, Vol. 13, No. 1 ( 2014-01), p. 41-54
    Abstract: Clinical trials rarely, if ever, occur in a vacuum. Generally, large amounts of clinical data are available prior to the start of a study, particularly on the current study's control arm. There is obvious appeal in using (i.e., ‘borrowing’) this information. With historical data providing information on the control arm, more trial resources can be devoted to the novel treatment while retaining accurate estimates of the current control arm parameters. This can result in more accurate point estimates, increased power, and reduced type I error in clinical trials, provided the historical information is sufficiently similar to the current control data. If this assumption of similarity is not satisfied, however, one can acquire increased mean square error of point estimates due to bias and either reduced power or increased type I error depending on the direction of the bias. In this manuscript, we review several methods for historical borrowing, illustrating how key parameters in each method affect borrowing behavior, and then, we compare these methods on the basis of mean square error, power and type I error. We emphasize two main themes. First, we discuss the idea of ‘dynamic’ (versus ‘static’) borrowing. Second, we emphasize the decision process involved in determining whether or not to include historical borrowing in terms of the perceived likelihood that the current control arm is sufficiently similar to the historical data. Our goal is to provide a clear review of the key issues involved in historical borrowing and provide a comparison of several methods useful for practitioners. Copyright © 2013 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 1539-1604 , 1539-1612
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 2083706-9
    detail.hit.zdb_id: 2163550-X
    SSG: 15,3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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