Journal of biopharmaceutical statistics
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A useful testing strategy in phase III trials: combined test of superiority and test of equivalence.
A useful testing strategy is proposed for a confirmatory phase III clinical trial. It consists of a combined test of superiority and test of equivalence, and it is easy to apply. ⋯ It is shown that the procedure needs no adjustment for multiplicity from the point of view of the closed testing procedure. The relationship between this strategy and a confidence interval is also discussed.
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The continual reassessment method (1) (CRM) for phase I cancer trials provides improved estimation of the maximum tolerated dose (MTD), and fewer patients receive ineffective dose levels compared to the traditionally used design. However, the CRM has not gained acceptance in practice owing to concerns with administering dose levels that are too toxic. ⋯ The result is a procedure that improves estimation of the MTD and decreases the use of ineffective doses, without significantly increasing the use of toxic dose levels. The CRM with modification outperforms the traditional method in a simulation study.
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Suppose, in a clinical trial, the interest is to show that the effectiveness of an experimental therapy is no worse than that of the standard therapy by more than a specified amount, say delta units. Blackwelder (1) discussed this problem in clinical trials where the outcome of interest is dichotomous. ⋯ In this paper, the asymmetric procedure of DeMets and Ware is modified to handle the case delta > 0. A two-stage procedure is considered in a drug interaction study that focuses on a specific side effect as the event of interest.
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The probabilistic rationale for statistical design and analysis of clinical trials is random assignment. While arithmetic and mathematical formulations may be identical to those used with random samples, we should not indiscriminately borrow tools from survey sample methods. Specifically, the confidence interval should be used sparingly, if at all. ⋯ The clinical trial is the model research tool for clinical medical research, founded on randomization. The confidence interval is a statistical tool for parameter estimation based on population sampling concepts. These tools are incompatible.
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Statistical estimates and significance tests address distinct (but related) questions using the same data. Point estimates and confidence intervals of differences are statistical estimates that address: "How LARGE is the difference in the population of interest?" A significance test addresses the question: "How LIKELY was the difference to have occurred by chance?" Because p-values deal with the existence of a real nonzero difference between treatments but not the size of that treatment difference, they cannot be used to assess clinical (practical) significance. ⋯ The point estimate is the outcome difference actually observed in the study sample; it is also the best single-number estimate of the unknown difference in the sampled population. Point estimates, confidence intervals, and p-values extract complementary information from study data and should all be reported for major results.