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  • measurement theory  (1)
  • power model  (1)
  • 1
    ISSN: 1573-904X
    Keywords: bioequivalence ; absorption rate ; Tmax ; absorption process rate ; measurement theory ; inference
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Purpose. Peak drug concentration (Cmax) measures the extremity of drug exposure and is a secondary indicator of the extent of absorption after area under the concentration time curve (AUC). Cmax serves as the indicator of absorption rate in bioequivalence (BE) studies in the US (1). The use of Cmax, not the time to Cmax(Tmax), as the metric to assess absorption rate causes erratic inferences in BE studies, and incorrect conclusions for some. We can improve BE efficiency (i.e., get the answer right the first time), by properly analyzing the time to Cmax(Tmax) instead of Cmax. Methods. We have previously redirected attention to Tmax as the unconfounded absorption rate variable, instead of Cmax, and have called for equally spaced sampling times during the suspected absorption phase to improve the performance of the rate metric (2). Equal spacing converts Tmax easily into a count variable and we illustrated an appropriate statistical analysis for counts. This paper provides some measurement theory concepts to help judge which is the more appropriate analysis, and also provides parametric confidence limits for Tmax treatment differences. Three separate BE studies are then analyzed by both methods. Results. By focusing on the differences in conclusions, or inferences, this paper identifies three major issues with the current FDA "recommended” analysis of BE studies. First, Cmax, a continuous variable peak-height or extent measure has usurped Tmax's function and performs erratically as a substitute measure for the rate of absorption. Second, Tmax, should be analyzed as a discrete attribute, not as a continuous variable. Third, since several extent measures (AUC, Cmax), not one, are actually being analyzed, an adjustment for multiple testing is mandatory if we are to maintain the size of the test at the desired α level (13), and not inadvertently use a narrower bioequivalence window than is intended. These actions all can have serious unintended consequences on inferences, including making inappropriate ones.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1573-904X
    Keywords: bioequivalence ; dose proportionality ; mixed effects model ; pharmacokinetics ; power model
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Purpose. The aim of this work was a pragmatic, statistically sound and clinically relevant approach to dose-proportionality analyses that is compatible with common study designs. Methods. Statistical estimation is used to derive a (1-α)% confidence interval (CI) for the ratio of dose-normalized, geometric mean values (Rdnm) of a pharmacokinetic variable (PK). An acceptance interval for Rdnm defining the clinically relevant, dose-proportional region is established a priori. Proportionality is declared if the CI for Rdnm is completely contained within the critical region. The approach is illustrated with mixed-effects models based on a power function of the form PK = β0 • Doseβ1; however, the logic holds for other functional forms. Results. It was observed that the dose-proportional region delineated by a power model depends only on the dose ratio. Furthermore, a dose ratio (ρ1) can be calculated such that the CI lies entirely within the pre-specified critical region. A larger ratio (ρ2) may exist such that the CI lies completely outside that region. The approach supports inferences about the PK response that are not constrained to the exact dose levels studied. Conclusion. The proposed method enhances the information from a clinical dose-proportionality study and helps to standardize decision rules.
    Type of Medium: Electronic Resource
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