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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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.
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The concept of controlling familywise type I and type II errors at the same time is essentially an integrated process to deal with multiplicity issues in clinical trials. The process will select a multiple testing procedure (MTP) which controls the familywise type I error and calculate the per hypothesis sample size such that the "studywise power" is maintained at desired level. The power of a study can be defined in several ways and it depends on the objective. ⋯ We also proposed the novel Bonferroni+ and optimal Bonferroni+ procedures to allocate per hypothesis type II error. We demonstrated the value of the proposed work as it cannot be replaced by simple simulations. A real clinical trial is discussed throughout the article as an example.
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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.