Journal of biopharmaceutical statistics
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The cut point of the immunogenicity screening assay is the level of response of the immunogenicity screening assay at or above which a sample is defined to be positive and below which it is defined to be negative. The Food and Drug Administration Guidance for Industry on Assay Development for Immunogenicity Testing of Therapeutic recommends the cut point to be an upper 95 percentile of the negative control patients. In this article, we assume that the assay data are a random sample from a normal distribution. ⋯ The selected methods evaluated for the immunogenicity screening assay cut-point determination are sample normal percentile, the exact lower confidence limit of a normal percentile (Chakraborti and Li, 2007) and the approximate lower confidence limit of a normal percentile. It is shown that the actual coverage probability for the lower confidence limit of a normal percentile using approximate normal method is much larger than the required confidence level with a small number of assays conducted in practice. We recommend using the exact lower confidence limit of a normal percentile for cut-point determination.
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According to ICH Q6A (1999), a specification is defined as a list of tests, references to analytical procedures, and appropriate acceptance criteria, which are numerical limits, ranges, or other criteria for the tests described. For drug products, specifications usually consist of test methods and acceptance criteria for assay, impurities, pH, dissolution, moisture, and microbial limits, depending on the dosage forms. They are usually proposed by the manufacturers and subject to the regulatory approval for use. ⋯ In this article, we describe and discuss the statistical issues of commonly used approaches in setting or revising specifications (usually tighten the limits), including reference interval, (Min, Max) method, tolerance interval, and confidence limit of percentiles. We also compare their performance in terms of the interval width and the intended coverage. Based on our study results and review experiences, we make some recommendations on how to select the appropriate statistical methods in setting product specifications to better ensure the product quality.
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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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This article reviews nonparametric alternatives to the mixed model normal theory analysis for the analyses of multicenter clinical trials. Under a mixed model, the traditional analysis is based on maximum likelihood theory under normal errors. This analysis, though, is not robust to outliers. ⋯ These rank-based analyses offer a complete analysis, including estimation of fixed effects and their standard errors, and tests of linear hypotheses. Both rank-based estimates of contrasts and individual treatment effects are reviewed. We illustrate the analyses using real data from a clinical trial.
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In 1998, the International Conference on Harmonization (ICH) published a guidance to facilitate the registration of medicines among ICH regions including the European Union, the United States, and Japan by recommending a framework for evaluating the impact of ethnic factors on a medicine's effect, such as its efficacy and safety at a particular dosage and dose regimen (ICH E5, 1998). The purpose of ICH E5 is not only to evaluate the ethnic factor influence on safety, efficacy, dosage, and dose regimen, but also more importantly to minimize duplication of clinical data and allow extrapolation of foreign clinical data to a new region. In this article, statistical methods for evaluation of bridging studies based on the concepts of consistency (Shih, 2001), reproducibility/generalizability (Shao and Chow, 2002), the weighted Z-tests for the design of bridging studies (Lan et al., 2005), and similarity between the new and original region based in terms of positive treatment effect (Hsiao et al., 2007) are studied. The relative merits and disadvantages of these methods are compared by several examples.