Journal of biopharmaceutical statistics
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Whether confirmatory or exploratory in nature, the investigation of subgroups poses statistical and interpretational challenges, yet these investigations can have important consequences for product licensing, labeling, reimbursement, and prescribing decisions. This article provides a high-level, nontechnical summary of key statistical issues in the analysis of subgroups, with a focus on the regulatory context in which drug development and licensing decisions are made. References to specific aspects of regulatory processes are based on the system in Europe, though it is hoped that the principles outlined can be generally applied to other regulatory regions. ⋯ Investigations into subgroups are unavoidable, yet subgroup analyses suffer from fundamental complications and limitations of which those planning and interpreting clinical trials must be aware. Some areas for further methodological work and an improved methodological framework for the conduct of exploratory subgroup analyses are discussed. Above all, the need for an integrated scientific approach is highlighted.
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Noninferiority (NI) clinical trials are getting a lot of attention of late due to their direct application in biosimilar studies. Because of the missing placebo arm, NI is an indirect approach to demonstrate efficacy of a test treatment. One of the key assumptions in the NI test is the constancy assumption, that is, that the effect of the reference treatment is the same in current NI trials as in historical superiority trials. ⋯ In this article, we propose four new NI methods and compare them with two existing methods to evaluate the change of background constancy assumption on the performance of these six methods. To achieve this goal, we study the impact of three elements-(1) strength of covariate, (2) degree of interaction between covariate and treatment, and (3) differences in distribution of the covariate between historical and current trials-on both the type I error rate and power using three different measures of association: difference, log relative risk, and log odds ratio. Based on this research, we recommend using a modified covariate-adjustment fixed margin method.
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A discrete random effects model (Lan and Pinheiro, 2012) was proposed recently for multiregional clinical trials for continuous responses. This article elucidates further the application of this model to time-to-event and binary responses. We provide some guidelines on how to design multiregional trials and also show how the same model lends itself naturally to meta-analysis.
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Past decades have seen a rapid growth of biopharmaceutical products on the market. The administration of such large molecules can generate antidrug antibodies that can induce unwanted immune reactions in the recipients. Assessment of immunogenicity is required by regulatory agencies in clinical and nonclinical development, and this demands a well-validated assay. ⋯ To precisely determine the cut point, a sufficiently large data set is often needed. However, there is no guideline other than some rule-of-thumb recommendations for sample size requirement in immunoassays. In this article, we propose a systematic approach to sample size determination for immunoassays and provide tables that facilitate its applications by scientists.