Product blood‐level in vivo bioequivalence (BE) studies typically involve complete blood concentration–time profiles generated for each subject. Accordingly, each subject provides the estimates of the rate and extent of drug absorption. However, repeated blood draws are not always feasible for studies using small animals because of handling or blood volume (e.g., fish or in toxicokinetic studies when only single samples can be taken per animal). Although several proposals have been published for comparing the product extent of absorption, the issue of comparative absorption rates remains unresolved. In this paper, we propose to apply the Bailer–Satterthwaite–Fieller confidence interval for estimating the ratio of partial area under the curve in studies that use a destructive sampling design (one blood sample per subject). To characterize the behavior of this alternative approach, we examine the impact of partial area cutoff time, blood sampling schedule, and the number of subjects included at each sampling time. Using simulated situations reflective of the issues encountered with immediate‐release veterinary formulations, we compare BE conclusions resulting from the use of this approach with simulated results that assuming repeated blood draws from each study subject.
We study the problem of estimation and inference on the average treatment effect in a smoking cessation trial where an outcome and some auxiliary information were measured longitudinally, and both were subject to missing values. Dynamic generalized linear mixed effects models linking the outcome, the auxiliary information, and the covariates are proposed. The maximum likelihood approach is applied to the estimation and inference on the model parameters. The average treatment effect is estimated by the G-computation approach, and the sensitivity of the treatment effect estimate to the nonignorable missing data mechanisms is investigated through the local sensitivity analysis approach. The proposed approach can handle missing data that form arbitrary missing patterns over time. We applied the proposed method to the analysis of the smoking cessation trial.
In a traditional blood level bioequivalence (BE) study, every subject provides drug concentrations at each blood sampling time. However, this approach is not suitable for animals whose blood volume limits or prohibits multiple sample collections. In our previous research, we presented an approach that can be applied to studies using a destructive sampling design where each animal provides only 1 blood sample that is then incorporated into a composite profile. Another situation we sometimes face is that of when the animals can contribute more than one sample but are still limited in the number of blood draws (e.g., 3) such that a complete profile per animal is not feasible. Unlike the destructive sampling situation, we cannot combine all blood samples into a single “composite” profile and ignore the correlation of values obtained from the same subject. To avoid the complexities associated with needing to include a covariance component among experimental units into the statistical model, we propose an approach whereby study subjects are randomly assigned to housing unit (e.g., cage or pen) and then randomly assigned to a sampling schedule within each housing unit. In doing so, housing unit rather than the individual subject serves as the experimental unit. This article provides an assessment of this alternative approach to assess product BE when only a limited number of samples can be obtained per study subject.
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