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  • 1
    Online Resource
    Online Resource
    Wiley ; 2011
    In:  Pharmaceutical Statistics Vol. 10, No. 6 ( 2011-11), p. 508-516
    In: Pharmaceutical Statistics, Wiley, Vol. 10, No. 6 ( 2011-11), p. 508-516
    Abstract: Modelling and simulation (M & S) is increasingly being applied in (clinical) drug development. It provides an opportune area for the community of pharmaceutical statisticians to pursue. In this article, we highlight useful principles behind the application of M & S. We claim that M & S should be focussed on decisions, tailored to its purpose and based in applied sciences, not relying entirely on data‐driven statistical analysis. Further, M & S should be a continuous process making use of diverse information sources and applying Bayesian and frequentist methodology, as appropriate. In addition to forming a basis for analysing decision options, M & S provides a framework that can facilitate communication between stakeholders. Besides the discussion on modelling philosophy, we also describe how standard simulation practice can be ineffective and how simulation efficiency can often be greatly improved. Copyright © 2011 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 1539-1604 , 1539-1612
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2011
    detail.hit.zdb_id: 2083706-9
    detail.hit.zdb_id: 2163550-X
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2003
    In:  Pharmaceutical Statistics Vol. 2, No. 2 ( 2003-04), p. 113-125
    In: Pharmaceutical Statistics, Wiley, Vol. 2, No. 2 ( 2003-04), p. 113-125
    Abstract: The option to stop a project is fundamental in drug development. The majority of drugs do not reach the market. Furthermore, many marketed drugs do not repay their development costs. It is therefore crucial to optimize the value of the option to stop. We formulate two examples of statistical models. One is based on success/failure in a series of trials; the other assumes that the commercial value evolves as a stochastic process as more information becomes available. These models are used to study a number of issues: the number and timing of decision points; value of information; speed of development; and order of trials. The results quantify the value of options. They show that early information that can change key decisions is most valuable. That is, we should nip bad projects in the bud. Modelling is also useful to analyse more complex decisions, for example, weighting the value of decision points against the cost of information or the speed of development. Copyright © 2003 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 1539-1604 , 1539-1612
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2003
    detail.hit.zdb_id: 2083706-9
    detail.hit.zdb_id: 2163550-X
    SSG: 15,3
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  • 3
    Online Resource
    Online Resource
    Wiley ; 2006
    In:  Biometrics Vol. 62, No. 3 ( 2006-09), p. 680-683
    In: Biometrics, Wiley, Vol. 62, No. 3 ( 2006-09), p. 680-683
    Type of Medium: Online Resource
    ISSN: 0006-341X
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2006
    detail.hit.zdb_id: 2054197-1
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  • 4
    Online Resource
    Online Resource
    Wiley ; 2021
    In:  Clinical Pharmacology & Therapeutics Vol. 110, No. 2 ( 2021-08), p. 311-320
    In: Clinical Pharmacology & Therapeutics, Wiley, Vol. 110, No. 2 ( 2021-08), p. 311-320
    Abstract: For the development of coronavirus disease 2019 (COVID‐19) drugs during the ongoing pandemic, speed is of essence whereas quality of evidence is of paramount importance. Although thousands of COVID‐19 trials were rapidly started, many are unlikely to provide robust statistical evidence and meet regulatory standards (e.g., because of lack of randomization or insufficient power). This has led to an inefficient use of time and resources. With more coordination, the sheer number of patients in these trials might have generated convincing data for several investigational treatments. Collaborative platform trials, comparing several drugs to a shared control arm, are an attractive solution. Those trials can utilize a variety of adaptive design features in order to accelerate the finding of life‐saving treatments. In this paper, we discuss several possible designs, illustrate them via simulations, and also discuss challenges, such as the heterogeneity of the target population, time‐varying standard of care, and the potentially high number of false hypothesis rejections in phase II and phase III trials. We provide corresponding regulatory perspectives on approval and reimbursement, and note that the optimal design of a platform trial will differ with our societal objective and by stakeholder. Hasty approvals may delay the development of better alternatives, whereas searching relentlessly for the single most efficacious treatment may indirectly diminish the number of lives saved as time is lost. We point out the need for incentivizing developers to participate in collaborative evidence‐generation initiatives when a positive return on investment is not met.
    Type of Medium: Online Resource
    ISSN: 0009-9236 , 1532-6535
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2040184-X
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  • 5
    In: Helicobacter, Wiley, Vol. 5, No. 4 ( 2000-12), p. 196-201
    Abstract: Background. Helicobacter pylori eradication with omeprazole, amoxycillin, and metronidazole is both effective and inexpensive. However, eradication rates with different dosages and dosing vary, and data on the impact of resistance are sparse. In this study, three different dosages of omeprazole, amoxycillin, and metronidazole were compared, and the influence of metronidazole resistance on eradication was assessed. Methods. Patients (n = 394) with a positive H. pylori screening test result and endoscopy‐proven duodenal ulcer in the past were enrolled into a multicenter study performed in four European countries and Canada. After baseline endoscopy, patients were randomly assigned to treatment for 1 week with either omeprazole, 20 mg twice daily, plus amoxycillin, 1,000 mg twice daily, plus metronidazole, 400 mg twice daily (low M); or omeprazole, 40 mg once daily, plus amoxycillin, 500 mg three times daily, plus metronidazole, 400 mg three times daily (medium M); or omeprazole, 20 mg twice daily, plus amoxycillin, 1,000 mg twice daily, plus metronidazole, 800 mg twice daily (high M). H. pylori status at entry was assessed by a 13 C urea breath test and a culture. Eradication was defined as two negative 13 C‐urea breath test results 4 and 8 weeks after therapy. Susceptibility testing using the agar dilution method was performed at entry and in patients with persistent infection after therapy. Results. The eradication rates, in terms of intention to treat (ITT) (population n = 379) (and 95% confidence interval [CI]) were as follows: low M 76% (68%, 84%), medium M 76% (68%, 84%), and high M 83% (75%, 89%). By per‐protocol analysis (population n = 348), the corresponding eradication rates were: low M 81%, medium M 80%, and high M 85%. No H. pylori strains were found to be resistant to amoxycillin. Prestudy resistance of H. pylori strains to metronidazole was found in 72 of 348 (21%) of the cultures at entry (range, 10%–39% in the five countries). The overall eradication rate in prestudy metronidazole‐susceptible strains was 232 of 266 (87%) and, for resistant strains, it was 41 of 70 (57%; p 〈 .001). Within each group, the results were as follows (susceptible/resistant): low M, 85%/54%; medium M, 86%/50%; and high M, 90%/75%. There were no statistically significant differences among the treatment groups. 23 strains susceptible to metronidazole before treatment were recultured after therapy failed; 20 of these had now developed resistance. Conclusions. H. pylori eradication rates were similar (approximately 80%) with all three regimens. Metronidazole resistance reduced efficacy; increasing the dose of metronidazole appeared not to overcome the problem or significantly improve the outcome. Treatment failure was generally associated with either prestudy or acquired metronidazole resistance. These findings are of importance when attempting H. pylori eradication in communities with high levels of metronidazole resistance.
    Type of Medium: Online Resource
    ISSN: 1083-4389 , 1523-5378
    Language: English
    Publisher: Wiley
    Publication Date: 2000
    detail.hit.zdb_id: 2020336-6
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  • 6
    Online Resource
    Online Resource
    Wiley ; 2010
    In:  Statistics in Medicine Vol. 29, No. 7-8 ( 2010-03-30), p. 797-807
    In: Statistics in Medicine, Wiley, Vol. 29, No. 7-8 ( 2010-03-30), p. 797-807
    Abstract: Many modern adaptive designs apply an analysis where p ‐values from different stages are weighted together to an overall hypothesis test. One merit of this combination approach is that the design can be made very flexible. However, combination tests violate the sufficiency and conditionality principles. As a consequence, combination tests may lead to absurd conclusions, such as ‘proving’ a positive effect while the average effect is negative. We explore the possibility of modifying the test so that such illogical conclusions are no longer possible. The dual test requires both the weighted combination test and a naïve test, ignoring the adaptations, to be statistically significant. The result is that the flexibility and type I error level control of the combination test are preserved, while the naïve test adds a safeguard against unconvincing results. The dual test is, by construction, at least as conservative as the combination test. However, many design changes will not lead to any power loss. A typical situation where the combination approach can be used is two‐stage sample size reestimation (SSR). For this case, we give a complete specification of all sample size modifications for which the two tests are equally powerful. We also study the overall power loss for some suggested SSR rules. Rules based on conditional power generally lead to ignorable power loss while a decision analytic approach exhibits clear discrepancies between the two tests. Copyright © 2010 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2010
    detail.hit.zdb_id: 1491221-1
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  • 7
    Online Resource
    Online Resource
    Wiley ; 2019
    In:  Statistics in Medicine Vol. 38, No. 20 ( 2019-09-10), p. 3782-3790
    In: Statistics in Medicine, Wiley, Vol. 38, No. 20 ( 2019-09-10), p. 3782-3790
    Abstract: We propose a new class of weighted logrank tests (WLRTs) that control the risk of concluding that a new drug is more efficacious than standard of care, when, in fact, it is uniformly inferior. Perhaps surprisingly, this risk is not controlled for WLRT in general. Tests from this new class can be constructed to have high power under a delayed‐onset treatment effect scenario, as well as being almost as efficient as the standard logrank test under proportional hazards.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1491221-1
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  • 8
    Online Resource
    Online Resource
    Wiley ; 2009
    In:  Drug Development Research Vol. 70, No. 3 ( 2009-05), p. 169-190
    In: Drug Development Research, Wiley, Vol. 70, No. 3 ( 2009-05), p. 169-190
    Type of Medium: Online Resource
    ISSN: 0272-4391 , 1098-2299
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2009
    detail.hit.zdb_id: 1500191-X
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  • 9
    Online Resource
    Online Resource
    Wiley ; 2022
    In:  Clinical Pharmacology & Therapeutics Vol. 112, No. 6 ( 2022-12), p. 1183-1190
    In: Clinical Pharmacology & Therapeutics, Wiley, Vol. 112, No. 6 ( 2022-12), p. 1183-1190
    Abstract: Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data‐driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.
    Type of Medium: Online Resource
    ISSN: 0009-9236 , 1532-6535
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2040184-X
    SSG: 15,3
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  • 10
    Online Resource
    Online Resource
    Wiley ; 2015
    In:  Biometrical Journal Vol. 57, No. 1 ( 2015-01), p. 64-75
    In: Biometrical Journal, Wiley, Vol. 57, No. 1 ( 2015-01), p. 64-75
    Abstract: This paper focuses on the concept of optimizing a multiple testing procedure (MTP) with respect to a predefined utility function. The class of Bonferroni‐based closed testing procedures, which includes, for example, (weighted) Holm, fallback, gatekeeping, and recycling/graphical procedures, is used in this context. Numerical algorithms for calculating expected utility for some MTPs in this class are given. The obtained optimal procedures, as well as the gain resulting from performing an optimization are then examined in a few, but informative, examples.
    Type of Medium: Online Resource
    ISSN: 0323-3847 , 1521-4036
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 131640-0
    detail.hit.zdb_id: 1479920-0
    SSG: 12
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