Journal of biopharmaceutical statistics
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This paper proposes several Concordance Correlation Coefficient (CCC) indices to measure the agreement among k raters, with each rater having multiple (m) readings from each of the n subjects for continuous and categorical data. In addition, for normal data, this paper also proposes the coverage probability (CP) and total deviation index (TDI). Those indices are used to measure intra, inter and total agreement among all raters. ⋯ When m = 1, the proposed estimate also reduces to the OCCC proposed by Lin (1989), King and Chinchilli (2001a) and Barnhart et al. (2002). When m = 1 and k = 2, the proposed estimate reduces to the original CCC proposed by Lin (1989). For categorical data, when k = 2 and m = 1, the proposed estimate and its associated inference reduce to the kappa for binary data and weighted kappa with squared weight for ordinal data.
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Comparative Study
Estimation of the probability of passing the USP dissolution test.
To ensure that a drug product will meet standards for identity, strength and stability as specified in the United States Pharmacopedia and National Formulary (USP/NF), it needs to pass a number of tests such as the content uniformity test and dissolution test at various stages of the manufacturing process. The sponsors usually have in-house specification limits based on some lower bounds of the probabilities of passing USP/NF tests to make sure that there is a high probability of passing the tests. ⋯ For the population mean and variance in some specified range, the probability derived from the methodology is very close to the exact probability. Therefore, the proposed method can provide an easy and accurate way to calculate the probability.
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We consider a dose-finding trial in phase IIB of drug development. For choosing an appropriate design for this trial the specification of two points is critical: an appropriate model for describing the dose-effect relationship, and the specification of the aims of the trial (objectives), which will be the focus in the present paper. For many situations it is essential to have a robust trial objective that has little risk of changing during the complete trial due to external information. ⋯ This implies either a gain in information, or essential savings in sample size. Further, we investigate an adaptive Bayesian optimal design that uses different optimal designs before and after an interim analysis, and we compare the adaptive with the nonadaptive Bayesian optimal design. The basic concept is illustrated using a modification of a recent AstraZeneca trial.
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In order to assess the equivalence of two treatments, clinical trials are designed to test against the null hypothesis that the difference (or ratio) of two means (proportions) is either smaller than a pre-specified lower equivalence limit or larger than a pre-specified upper equivalence limit. For example, in generic drug evaluation, such approach is defined as average bioequivalence. ⋯ The stochastic statement can then be generalized to define the probability of exchangeability (i.e., coverage percentage) of the two treatments. The approach will be illustrated with a numeric example.