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    Statistical Inferences of Two-Stage Phase II Cancer Clinical Trials with Two Co-primary Endpoints

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    Sun, Yiming
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    Abstract
    In cancer research, two-stage designs are usually used to assess the effect of a new

    agent in phase II clinical trials. The optimal two-stage designs with two co-primary

    endpoints have been proposed to assess the effects of new cancer treatments, such

    as cytostatic or molecularly targeted agents (MTAs), based on both response rate

    and early progression rate. Statistical inference procedures, such as, point estima

    tion, p-value, and confidence region, for the true response rate and early progression

    rate based on the data from the phase II trials conducted according to the optimal

    two-stage designs would be very useful for further testing of the agents in phase III

    trials but have not been addressed in the literature. In this thesis, I first provide a

    review of the optimal two-stage design for phase II clinical trials with one endpoint

    and statistical inference procedures developed for this design. Then I propose some

    new statistical inference procedures for the optimal two-stage design of phase II clin

    ical trials with two co-primary endpoints, which include naive maximum likelihood

    estimate (MLE), bias-corrected estimates, and uniformly minimum variance unbiased

    estimate (UMVUE) for the point estimation, naive p-value and likelihood ratio test

    (LRT) based p-value for the hypothesis testing, and LRT based confidence region.

    Simulation studies were performed to evaluate the performance of these procedures.
    URI for this record
    http://hdl.handle.net/1974/24840
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    • Department of Mathematics and Statistics Graduate Theses
    • Queen's Graduate Theses and Dissertations
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