Journal of biopharmaceutical statistics
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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.
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Personalized medicine is an area of growing attention in medical research and practice. A market-ready companion diagnostic test (CDx) is used in personalized medicine for identifying the best treatment for an individual patient. Unfortunately, development of CDx may lag behind the development of the drug, and consequently we use a clinical trial assay (CTA) to enroll patients into the drug pivotal clinical trial instead. ⋯ Particularly, we aim to use the propensity score method with doubly robust estimation and optimal matching to address the challenge. We extend under a current framework on drug efficacy estimation in the CDx IU population, using data from both the bridging study and the CTA drug pivotal clinical trial. Both approaches are discussed in the context of a randomized bridging study, and a targeted design clinical trial with simulations, followed by analyzing simulated data that mimic a real ongoing clinic trial.
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The world of medical devices while highly diverse is extremely innovative, and this facilitates the adoption of innovative statistical techniques. Statisticians in the Center for Devices and Radiological Health (CDRH) at the Food and Drug Administration (FDA) have provided leadership in implementing statistical innovations. The innovations discussed include: the incorporation of Bayesian methods in clinical trials, adaptive designs, the use and development of propensity score methodology in the design and analysis of non-randomized observational studies, the use of tipping-point analysis for missing data, techniques for diagnostic test evaluation, bridging studies for companion diagnostic tests, quantitative benefit-risk decisions, and patient preference studies.