The Experts below are selected from a list of 39036 Experts worldwide ranked by ideXlab platform

Erik Hertervig - One of the best experts on this subject based on the ideXlab platform.

Carl Eriksson - One of the best experts on this subject based on the ideXlab platform.

Sven Almer - One of the best experts on this subject based on the ideXlab platform.

C Malmgren - One of the best experts on this subject based on the ideXlab platform.

Gerhard Hommel - One of the best experts on this subject based on the ideXlab platform.

  • Strategies for Including Patients Recruited During Interim Analysis of Clinical Trials
    Journal of biopharmaceutical statistics, 2007
    Co-Authors: Andreas Faldum, Gerhard Hommel
    Abstract:

    In clinical trials a periodical check of safety and efficacy data is often needed. For organizational reasons it is rarely desirable to stop a trial during such an Interim Analysis. Therefore, new study patients are included in the trial while the Interim Analysis is ongoing. Disregarding the additional information provided by these Interim patients would be unsatisfactory, especially for an office of regulatory affairs. Consequently, the rules for group sequential or adaptive decisions must be adjusted to the recruitment of Interim patients. In this paper, two strategies for modifying study designs to consider the Analysis of Interim patients are proposed.

  • Performance of adaptive sample size adjustment with respect to stopping criteria and time of Interim Analysis
    Statistics in medicine, 2007
    Co-Authors: Antje Jahn-eimermacher, Gerhard Hommel
    Abstract:

    The benefit of adjusting the sample size in clinical trials on the basis of treatment effects observed in Interim Analysis has been the subject of several recent papers. Different conclusions were drawn about the usefulness of this approach for gaining power or saving sample size, because of differences in trial design and setting. We examined the benefit of sample size adjustment in relation to trial design parameters such as 'time of Interim Analysis' and 'choice of stopping criteria'. We compared the adaptive weighted inverse normal method with classical group sequential methods for the most common and for optimal stopping criteria in early, half-time and late Interim analyses. We found that reacting to Interim data might significantly reduce average sample size in some situations, while classical approaches can out-perform the adaptive designs under other circumstances. We characterized these situations with respect to time of Interim Analysis and choice of stopping criteria.

  • Adaptive Modifications of Hypotheses After an Interim Analysis
    Biometrical Journal, 2001
    Co-Authors: Gerhard Hommel
    Abstract:

    It is investigated how one can modify hypotheses in a trial after an Interim Analysis such that the type I error rate is controlled. If only a global statement is desired, a solution was given by Bauer (1989). For a general multiple testing problem, Kieser, Bauer and Lehmacher (1999) and Bauer and Kieser (1999) gave solutions, by means of which the initial set of hypotheses can be reduced after the Interim Analysis. The same techniques can be applied to obtain more flexible strategies, as changing weights of hypotheses, changing an a priori order, or even including new hypotheses. It is emphasized that the application of these methods requires very careful planning of a trial as well as a critical discussion of the scientific aims in order to avoid every manipulation.