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  • 1
    Online Resource
    Online Resource
    Wiley ; 2003
    In:  Biometrical Journal Vol. 45, No. 3 ( 2003-04), p. 292-310
    In: Biometrical Journal, Wiley, Vol. 45, No. 3 ( 2003-04), p. 292-310
    Abstract: Although linear rank statistics for the two‐sample problem are distribution free tests, their power depends on the distribution of the data. In the planning phase of an experiment, researchers are often uncertain about the shape of this distribution and so the choice of test statistic for the analysis and the determination of the required sample size are based on vague information. Adaptive designs with interim analysis can potentially overcome both problems. And in particular, adaptive tests based on a selector statistic are a solution to the first. We investigate whether adaptive tests can be usefully implemented in flexible two‐stage designs to gain power. In a simulation study, we compare several methods for choosing a test statistic for the second stage of an adaptive design based on interim data with the procedure that applies adaptive tests in both stages. We find that the latter is a sensible approach that leads to the best results in most situations considered here. The different methods are illustrated using a clinical trial example.
    Type of Medium: Online Resource
    ISSN: 0323-3847 , 1521-4036
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2003
    detail.hit.zdb_id: 131640-0
    detail.hit.zdb_id: 1479920-0
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    Wiley ; 1996
    In:  Biometrical Journal Vol. 38, No. 8 ( 1996-01), p. 941-949
    In: Biometrical Journal, Wiley, Vol. 38, No. 8 ( 1996-01), p. 941-949
    Abstract: In a recent paper, Browne (1995) investigated the use of a pilot sample for sample size calculation. Monte Carlo simulations indicated that using a 100 · (1 — γ) per cent upper one‐sided confidence limit on the population varíance σ 2 leads to a sample size that guarantees the planned power with a probability of at least 1 − γ. The purpose of this paper is to get further insight into the results of Browne by analytical considerations. Furthermore, the expected power is investigated when applying the strategy and recommendations for the choice of the pilot sample size are given.
    Type of Medium: Online Resource
    ISSN: 0323-3847 , 1521-4036
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 1996
    detail.hit.zdb_id: 131640-0
    detail.hit.zdb_id: 1479920-0
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    Wiley ; 2019
    In:  Biometrical Journal Vol. 61, No. 2 ( 2019-03), p. 357-378
    In: Biometrical Journal, Wiley, Vol. 61, No. 2 ( 2019-03), p. 357-378
    Abstract: The conduct of phase II and III programs is costly, time‐consuming and, due to high failure rates in late development stages, risky. There is a strong connection between phase II and III trials as the go/no‐go decision and the sample size chosen for phase III are based on the results observed in phase II. An integrated planning of phase II and III is therefore reasonable. The success of phase II/III programs crucially depends on the allocation of the resources to phase II and III in terms of sample size and the rule applied to decide whether to stop or to proceed with phase III. Recently, a utility‐based approach was proposed, where optimal planning of phase II/III programs is achieved by taking fixed and variable costs of the drug development program and potential gains after a successful launch into account. However, this method is restricted to programs with a single phase III trial, while regulatory authorities usually require statistical significance in two or more phase III trials. We present a generalization of this procedure to programs where two or more phase III trials are performed. Optimal phase II sample sizes and go/no‐go decision rules are provided for time‐to‐event outcomes and cases, where at least one, two, or three phase III trials need to be successful. Different drug development program strategies (e.g. one large vs. two phase III trials) are compared within these different cases. Application to practical examples typically met in oncology trials illustrates the proposed method.
    Type of Medium: Online Resource
    ISSN: 0323-3847 , 1521-4036
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 131640-0
    detail.hit.zdb_id: 1479920-0
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Online Resource
    Online Resource
    Wiley ; 2015
    In:  Statistics in Medicine Vol. 34, No. 13 ( 2015-06-15), p. 2128-2137
    In: Statistics in Medicine, Wiley, Vol. 34, No. 13 ( 2015-06-15), p. 2128-2137
    Abstract: Phase II studies in oncology are frequently conducted as two‐stage single‐arm trials with a binary endpoint indicating tumor response. As a common feature of these designs, the sample sizes of the two stages and the decision rules for the interim and the final analysis have to be pre‐specified and adhered to strictly during the course of the trial in order to assure control of the type I error rate. In practice, however, the attained sample sizes often deviate from the planned ones leading to the situation of overrunning or underrunning. The currently available approaches to deal with this problem are either based on assumptions that are rarely met in practice or do not guarantee that the significance level is kept. However, strict control of the type I error rate plays an important role also for single‐arm cancer trials, as they are frequently a fundamental part of the registration information. We propose a general methodology that allows handling both unintentional and intentional overrunning and underrunning while strictly controlling the type I error rate. Application of the proposed procedure and some of its characteristics are illustrated with a real phase II oncology trial. Copyright © 2015 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 843037-8
    detail.hit.zdb_id: 1491221-1
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  • 5
    Online Resource
    Online Resource
    Wiley ; 2016
    In:  Statistics in Medicine Vol. 35, No. 2 ( 2016-01-30), p. 305-316
    In: Statistics in Medicine, Wiley, Vol. 35, No. 2 ( 2016-01-30), p. 305-316
    Abstract: Phase II and phase III trials play a crucial role in drug development programs. They are costly and time consuming and, because of high failure rates in late development stages, at the same time risky investments. Commonly, sample size calculation of phase III is based on the treatment effect observed in phase II. Therefore, planning of phases II and III can be linked. The performance of the phase II/III program crucially depends on the allocation of the resources to phases II and III by appropriate choice of the sample size and the rule applied to decide whether to stop the program after phase II or to proceed. We present methods for a program‐wise phase II/III planning that aim at determining optimal phase II sample sizes and go/no‐go decisions in a time‐to‐event setting. Optimization is based on a utility function that takes into account (fixed and variable) costs of the drug development program and potential gains after successful launch. The proposed methods are illustrated by application to a variety of scenarios typically met in oncology drug development. Copyright © 2015 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 843037-8
    detail.hit.zdb_id: 1491221-1
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    In: Statistics in Medicine, Wiley, Vol. 36, No. 15 ( 2017-07-10), p. 2378-2390
    Type of Medium: Online Resource
    ISSN: 0277-6715
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 843037-8
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    Online Resource
    Online Resource
    Wiley ; 2017
    In:  Statistics in Medicine Vol. 36, No. 25 ( 2017-11-10), p. 3935-3947
    In: Statistics in Medicine, Wiley, Vol. 36, No. 25 ( 2017-11-10), p. 3935-3947
    Abstract: Adaptive enrichment designs are an attractive option for clinical trials that aim at demonstrating efficacy of therapies, which may show different benefit for the full patient population and a prespecified subgroup. In these designs, based on interim data, either the subgroup or the full population is selected for further exploration. When selection is based on efficacy data, this introduces bias to the commonly used maximum likelihood estimator. For the situation of two‐stage designs with a single prespecified subgroup, we present six alternative estimators and investigate their performance in a simulation study. The most consistent reduction of bias over the range of scenarios considered was achieved by a method combining the uniformly minimum variance conditionally unbiased estimator with a conditional moment estimator. Application of the methods is illustrated by a clinical trial example.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 843037-8
    detail.hit.zdb_id: 1491221-1
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    In: Statistics in Medicine, Wiley, Vol. 31, No. 11-12 ( 2012-05-20), p. 1303-1305
    Abstract: International regulatory guidelines require that a ‘qualified statistician’ takes responsibility for the statistical aspects of a clinical trial used for drug licensing. No consensus on what constitutes a ‘qualified statistician’ appears to have been developed so far. The International Society for Clinical Biostatistics is issuing this reflection paper in order to stimulate a discussion on the concept. Copyright © 2011 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2012
    detail.hit.zdb_id: 843037-8
    detail.hit.zdb_id: 1491221-1
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    Online Resource
    Online Resource
    Wiley ; 2002
    In:  Statistics in Medicine Vol. 21, No. 2 ( 2002-01-30), p. 165-176
    In: Statistics in Medicine, Wiley, Vol. 21, No. 2 ( 2002-01-30), p. 165-176
    Abstract: When planning a clinical trial the sample size calculation is commonly based on an a priori estimate of the variance of the outcome variable. Misspecification of the variance can have substantial impact on the power of the trial. It is therefore attractive to update the planning assumptions during the ongoing trial using an internal estimate of the variance. For this purpose, an EM algorithm based procedure for blinded variance estimation was proposed for normally distributed data. Various simulation studies suggest a number of appealing properties of this procedure. In contrast, we show that (i) the estimates provided by this procedure depend on the initialization, (ii) the stopping rule used is inadequate to guarantee that the algorithm converges against the maximum likelihood estimator, and (iii) the procedure corresponds to the special case of simple randomization which, however, in clinical trials is rarely applied. Further, we show that maximum likelihood estimation leads to no reasonable results for blinded sample size re‐estimation due to bias and high variability. The problem is illustrated by a clinical trial in asthma. Copyright © 2002 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2002
    detail.hit.zdb_id: 843037-8
    detail.hit.zdb_id: 1491221-1
    Library Location Call Number Volume/Issue/Year Availability
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  • 10
    Online Resource
    Online Resource
    Wiley ; 2003
    In:  Statistics in Medicine Vol. 22, No. 6 ( 2003-03-30), p. 995-1007
    In: Statistics in Medicine, Wiley, Vol. 22, No. 6 ( 2003-03-30), p. 995-1007
    Abstract: Even in situations where the design and conduct of clinical trials is highly standardized, there may be a considerable between‐study variation in the observed variability of the primary outcome variable. As a consequence, performing a study in a fixed sample size design implies a considerable risk of resulting in a too high or too low sample size. This difficulty can be alleviated by applying a design with internal pilot study. After a provisional sample size calculation in the planning stage, a portion of the planned sample is recruited and the sample size is recalculated on the basis of the observed variability. To comply with the requirement of some regulatory guidelines only blinded data should be used for the reassessment procedure. Furthermore, the effect on the type I error rate should be quantified. The current literature presents analytical results on the actual level in the t ‐test situation only for superiority trials. In these situations, blinded sample size recalculation does not lead to an inflation of the type I error rate. We extended the methodology to non‐inferiority and equivalence trials with normally distributed outcome variable and hypotheses formulated in terms of the ratio and difference of means. Surprisingly, in contrast to the case of testing superiority, we observed actual type I error rates above the nominal level. The extent of inflation depends on the required sample size, the sample size of the internal pilot study, and the standardized equivalence or non‐inferiority margin. It turned out that the elevation of the significance level is negligible for most practical situations. Nevertheless, the consequences of sample size reassessment have to be discussed case by case and regulatory concerns with respect to the actual size of the procedure cannot generally be refuted by referring to the fact that only blinded data were used. Copyright © 2003 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2003
    detail.hit.zdb_id: 843037-8
    detail.hit.zdb_id: 1491221-1
    Library Location Call Number Volume/Issue/Year Availability
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