Journal of biopharmaceutical statistics
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In clinical studies, ordered categorical responses are common. To compare the efficacy of several treatments with a control for ordinal responses, the normal latent variable model has recently been proposed. This approach conceptualizes the responses as manifestations of an underlying continuous normal variable. ⋯ The proposed method is constructed such that the familywise type I error rate is controlled at a prespecified level. In addition, for a given level of test power, the procedure to evaluate the required sample size is provided. The proposed testing procedure is also illustrated by an example from a clinical study.
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This review article sets out to examine the Type I error rates used in noninferiority trials. Most papers regarding noninferiority trials only state Type I error rate without mentioning clearly which Type I error rate is evaluated. Therefore, the Type I error rate in one paper is often different from the Type I error rate in another paper, which can confuse readers and makes it difficult to understand papers. ⋯ The conditional across-trial Type I error rate is also briefly discussed. In noninferiority trials comparing a new treatment with an active control without a placebo arm, it is argued that the within-trial Type I error rate should be controlled in order to obtain approval of the new treatment from the regulatory agencies. I hope that this article can help readers understand the difference between two paradigms employed in noninferiority trials.
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Regulatory decisions are made based on the assessment of risk and benefit of medical devices at the time of pre-market approval and subsequently, when post-market risk-benefit balance needs reevaluation. Such assessments depend on scientific evidence obtained from pre-market studies, post-approval studies, post-market surveillance studies, patient perspective information, as well as other real world data such as national and international registries. ⋯ While these registries provide large quantities of data reflecting real world practice and can potentially reduce the cost of clinical trials, challenges arise concerning (1) data quality adequate for regulatory decision-making, (2) bias introduced at every stage and aspect of study, (3) scientific validity of study designs, and (4) reliability and interpretability of study results. This article will discuss related statistical and regulatory challenges and opportunities with examples encountered in medical device regulatory reviews.