Journal of biopharmaceutical statistics
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Due to the special nature of medical device clinical studies, observational (nonrandomized) comparative studies play important roles in the premarket safety/effectiveness evaluation of medical devices. While historical data collected in earlier investigational device exemption studies of a previously approved medical device have been used to form control groups in comparative studies, high-quality registry data are emerging to provide opportunities for the premarket evaluation of new devices. However, in such studies, various biases could be introduced in every stage and aspect of study and may compromise the objectivity of study design and validity of study results. In this article, challenges and opportunities in the design of such studies using propensity score methodology are discussed from regulatory perspectives.
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Recent progress in biosimilars development is overviewed, with attention to the history of issues and processes leading to current regulations, and to scientific considerations, including progress on design and operational implementation issues that arise and are peculiar to biosimilars trial design and implementation.
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This article proposes a general comparison model for assessing individual agreement of k ≥ 2 raters evaluating n subjects with m ≥ 2 replicated readings. Users can explore total-rater agreement relative to intrarater agreement where any subset of the k raters can be selected in the numerator and denominator. Users are also allowed to compare intrarater agreement among selected raters. ⋯ The method used by the Food and Drug Administration (FDA) for evaluating individual bioequivalence under relative scale becomes the special case of our approach. The IIR is a classical assessment such that the precision of selected raters can be better than; equal to; or worse than that of other raters. The estimation and statistical inference of TIR and IIR are obtained through GEE methodology.
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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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While randomized, well-controlled, clinical trials have been viewed as the gold standard in the evaluation of medical products, including drugs, biological products, and medical devices, it is not uncommon for safety assessment to be performed using observational studies, for ethical or practical reasons. In observational studies, various biases could be introduced in every stage and aspect of study, and consequently the resulting statistical inference may carry a lower level of scientific assurance, compared to randomized trials. To ensure the objectivity of study design and interpretability of the results, it is critical to address the challenges of such studies. In this paper, we share regulatory considerations on the prospective design of observational studies to address safety issues using propensity score methodology.