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Pharmaceutical statistics : The Journal of Applied... 10건

  1. [해외논문]   Bayesian applications in pharmaceutical statistics  

    Morgan, David
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. NA , 2018 , 1539-1604 ,

    초록

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  2. [해외논문]   Issue Information  


    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 295 - 297 , 2018 , 1539-1604 ,

    초록

    Abstract No abstract is available for this article.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  3. [해외논문]   Bayesian applications in pharmaceutical statistics  

    Morgan, David (Department of Pharmaceutical Medicine, King's College London, London, UK)
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 298 - 300 , 2018 , 1539-1604 ,

    초록

    Abstract No abstract is available for this article.

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  4. [해외논문]   Better decision making in drug development through adoption of formal prior elicitation  

    Dallow, Nigel (GlaxoSmithKline, Uxbridge, Middlesex, UK) , Best, Nicky (GlaxoSmithKline, Uxbridge, Middlesex, UK) , Montague, Timothy H (GlaxoSmithKline, Philadelphia, PA, USA)
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 301 - 316 , 2018 , 1539-1604 ,

    초록

    With the continued increase in the use of Bayesian methods in drug development, there is a need for statisticians to have tools to develop robust and defensible informative prior distributions. Whilst relevant empirical data should, where possible, provide the basis for such priors, it is often the case that limitations in data and/or our understanding may preclude direct construction of a data‐based prior. Formal expert elicitation methods are a key technique that can be used to determine priors in these situations. Within GlaxoSmithKline, we have adopted a structured approach to prior elicitation on the basis of the SHELF elicitation framework and routinely use this in conjunction with calculation of probability of success (assurance) of the next study(s) to inform internal decision making at key project milestones. The aim of this paper is to share our experiences of embedding the use of prior elicitation within a large pharmaceutical company, highlighting both the benefits and challenges of prior elicitation through a series of case studies. We have found that putting team beliefs into the shape of a quantitative probability distribution provides a firm anchor for all internal decision making, enabling teams to provide investment boards with formally appropriate estimates of the probability of trial success as well as robust plans for interim decision rules where appropriate. As an added benefit, the elicitation process provides transparency about the beliefs and risks of the potential medicine, ultimately enabling better portfolio and company‐wide decision making.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  5. [해외논문]   Practical experiences of adopting assurance as a quantitative framework to support decision making in drug development  

    Crisp, Adam (GlaxoSmithKline, Uxbridge, Middlesex, UK) , Miller, Sam (Exploristics, Belfast, UK) , Thompson, Douglas (GlaxoSmithKline, Stevenage, Hertfordshire, UK) , Best, Nicky (GlaxoSmithKline, Uxbridge, Middlesex, UK)
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 317 - 328 , 2018 , 1539-1604 ,

    초록

    All clinical trials are designed for success of their primary objectives. Hence, evaluating the probability of success (PoS) should be a key focus at the design stage both to support funding approval from sponsor governance boards and to inform trial design itself. Use of assurance —that is, expected success probability averaged over a prior probability distribution for the treatment effect—to quantify PoS of a planned study has grown across the industry in recent years, and has now become routine within the authors' company. In this paper, we illustrate some of the benefits of systematically adopting assurance as a quantitative framework to support decision making in drug development through several case‐studies where evaluation of assurance has proved impactful in terms of trial design and in supporting governance‐board reviews of project proposals. In addition, we describe specific features of how the assurance framework has been implemented within our company, highlighting the critical role that prior elicitation plays in this process, and illustrating how the overall assurance calculation may be decomposed into a sequence of conditional PoS estimates which can provide greater insight into how and when different development options are able to discharge risk.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  6. [해외논문]   How to use prior knowledge and still give new data a chance?  

    Weber, Kristina (Institute for Biostatistics, Hannover Medical School, Hanover, Germany) , Hemmings, Rob (MHRA, London, UK) , Koch, Armin (Institute for Biostatistics, Hannover Medical School, Hanover, Germany)
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 329 - 341 , 2018 , 1539-1604 ,

    초록

    A common challenge for the development of drugs in rare diseases and special populations, eg, paediatrics, is the small numbers of patients that can be recruited into clinical trials. Extrapolation can be used to support development and licensing in paediatrics through the structured integration of available data in adults and prospectively generated data in paediatrics to derive conclusions that support licensing decisions in the target paediatric population. In this context, Bayesian analyses have been proposed to obtain formal proof of efficacy of a new drug or therapeutic principle by using additional information (data, opinion, or expectation), expressed through a prior distribution. However, little is said about the impact of the prior assumptions on the evaluation of outcome and prespecified strategies for decision‐making as required in the regulatory context. On the basis of examples, we explore the use of data‐based Bayesian meta‐analytic–predictive methods and compare these approaches with common frequentist and Bayesian meta‐analysis models. Noninformative efficacy prior distributions usually do not change the conclusions irrespective of the chosen analysis method. However, if heterogeneity is considered, conclusions are highly dependent on the heterogeneity prior. When using informative efficacy priors based on previous study data in combination with heterogeneity priors, these may completely determine conclusions irrespective of the data generated in the target population. Thus, it is important to understand the impact of the prior assumptions and ensure that prospective trial data in the target population have an appropriate chance, to change prior belief to avoid trivial and potentially erroneous conclusions.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  7. [해외논문]   Bayesian approach for assessing noninferiority in a three‐arm trial with binary endpoint  

    Ghosh, Santu (Division of Biostatistics and Data Science, DPHS, Augusta University, GA, USA) , Tiwari, Ram C. (Division of Biostatistics, CDRH, US Food and Drug Administration, Silver Spring, MD, USA) , Ghosh, Samiran (Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI, USA)
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 342 - 357 , 2018 , 1539-1604 ,

    초록

    With the recent advancement in many therapeutic areas, quest for better and enhanced treatment options is ever increasing. While the “efficacy” metric plays the most important role in this development, emphasis on other important clinical factors such as less intensive side effects, lower toxicity, ease of delivery, and other less debilitating factors may result in the selection of treatment options, which may not beat current established treatment option in terms efficacy, yet prove to be desirable for subgroups of patients. The resultant clinical trial by means of which one establishes such slightly less efficacious treatment is known as noninferiority (NI) trial. Noninferiority trials often involve an active established comparator arm, along with a placebo and an experimental treatment arm, resulting into a 3‐arm trial. Most of the past developments in a 3‐arm NI trial consider defining a prespecified fraction of unknown effect size of reference drug, i.e., without directly specifying a fixed NI margin. However, in some recent developments, more direct approach is being considered with prespecified fixed margin, albeit in the frequentist setup. In this article, we consider Bayesian implementation of such trial when primary outcome of interest is binary. Bayesian paradigm is important, as it provides a path to integrate historical trials and current trial information via sequential learning. We use several approximation‐based and 2 exact fully Bayesian methods to evaluate the feasibility of the proposed approach. Finally, a clinical trial example is reanalyzed to demonstrate the benefit of the proposed approach.

    원문보기

    원문보기
    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  8. [해외논문]   Bayesian statistical models to estimate EQ‐5D utility scores from EORTC QLQ data in myeloma  

    Kharroubi, Samer A. (Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut, Lebanon) , Edlin, Richard (School of Population Health, University of Auckland, Auckland, New Zealand) , Meads, David (Academic Unit of Health Economics, University of Leeds, Leeds, UK) , McCabe, Christopher (Department of Emergency Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada)
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 358 - 371 , 2018 , 1539-1604 ,

    초록

    It is well documented that the modelling of health‐related quality of life data is difficult as the distribution of such data is often strongly right/left skewed and it includes a significant percentage of observations at one. The objective of this study is to develop a series of two‐part models (TPMs) that deal with these issues. Data from the UK Medical Research Council Myeloma IX trial were used to examine the relationship between the European Organization for Research and Treatment of Cancer (EORTC) QLQ‐C30/QLQ‐MY20 scores and the European QoL‐5 Dimensions (EQ‐5D) utility score. Four different TPMs were developed. The models fitted included TPM with normal regression, TPM with normal regression with variance a function of participant characteristics, TPM with log‐transformed data, and TPM with gamma regression and a log link. The cohort of 1839 patients was divided into 75% derivation sample, to fit the different models, and 25% validation sample to assess the predictive ability of these models by comparing predicted and observed mean EQ‐5D scores in the validation set, unadjusted R 2 , and root mean square error. Predictive performance in the derivation dataset depended on the criterion used, with R 2 /adjusted‐ R 2 favouring the TPM with normal regression and mean predicted error favouring the TPM with gamma regression. The TPM with gamma regression performs best within the validation dataset under all criteria. TPM regression models provide flexible approaches to estimate mean EQ‐5D utility weights from the EORTC QLQ‐C30/QLQ‐MY20 for use in economic evaluation.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  9. [해외논문]   Bayesian dose‐finding phase I trial design incorporating historical data from a preceding trial  

    Takeda, Kentaro (Data Science, Astellas Pharma Global Development, Inc., Illinois, USA) , Morita, Satoshi (Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan)
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 372 - 382 , 2018 , 1539-1604 ,

    초록

    We consider the problem of incorporating historical data from a preceding trial to design and conduct a subsequent dose‐finding trial in a possibly different population of patients. In oncology, for example, after a phase I dose‐finding trial is completed in Caucasian patients, investigators often conduct a further phase I trial to determine the maximum tolerated dose in Asian patients. This may be due to concerns about possible differences in treatment tolerability between populations. In this study, we propose to adaptively incorporate historical data into prior distributions assumed in a new dose‐finding trial. Our proposed approach aims to appropriately borrow strength from a previous trial to improve the maximum tolerated dose determination in another patient population. We define a “historical‐to‐current (H‐C)” parameter representing the degree of borrowing based on a retrospective analysis of previous trial data. In simulation studies, we examine the operating characteristics of the proposed method in comparison with 3 alternative approaches and assess how the H‐C parameter functions across a variety of realistic settings.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

    Fig. 1 이미지
  10. [해외논문]   BOIN‐ET: Bayesian optimal interval design for dose finding based on both efficacy and toxicity outcomes  

    Takeda, Kentaro (Data Science, Astellas Pharma Global Development, Inc., Northbrook, IL, USA) , Taguri, Masataka (Department of Biostatistics, Yokohama City University, Yokohama, Japan) , Morita, Satoshi (Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan)
    Pharmaceutical statistics : The Journal of Applied Statistics in the Pharmaceutical Industry v.17 no.4 ,pp. 383 - 395 , 2018 , 1539-1604 ,

    초록

    One of the main purposes of a phase I dose‐finding trial in oncology is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent phase II and III trials. Many phase I dose‐finding methods based solely on toxicity considerations have been proposed under the assumption that both toxicity and efficacy monotonically increase with the dose level. Such an assumption may not be necessarily the case, however, when evaluating the OD for molecular targeted, cytostatic, and biological agents, as well as immune‐oncology therapy. To address this issue, we extend the Bayesian optimal interval (BOIN) design, which is nonparametric and thus does not require the assumption used in model‐based designs, in order to identify an OD based on both efficacy and toxicity outcomes. The new design is named “BOIN‐ET.” A simulation study is presented that includes a comparison of this proposed method to the model‐based approaches in terms of both efficacy and toxicity responses. The simulation shows that BOIN‐ET has advantages in both the percentages of correct ODs selected and the average number of patients allocated to the ODs across a variety of realistic settings.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

    이미지

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