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Aims: For market approval, new drug formulations (test) must demonstrate bioequivalence (BE) to at least one approved formulation (reference). If several formulations of a drug are already on the market, one might have to show BE to more than one reference formulation. Similarly, if several test formulations have shown BE to a reference formulation, it will be of interest whether the test formulations are bioequivalent to each other. Methods: An enhanced statistical model to assess BE indirectly through a network meta-analysis is provided. Statistical properties of a parallel and a bridging approach are derived, in particular the relative statistical efficiency of the two approaches. The analysis is illustrated using individual subject data from two 3×3 crossover trials of metformin formulations, which have one of the formulations in common. Results: The parallel estimate of relative bioavailability is confounded with between-trial differences, while the bridging estimate is not. The standard errors of the formulation differences using the bridging approach are smaller than the standard errors using the parallel approach if the within-subject correlation in each trial of the network is larger than 0.5. This is the condition for a crossover trial to be more efficient than a parallel trial, and thus is usually fulfilled in pharmacokinetic crossover trials. Conclusions: Indirect BE assessment offers the opportunity to efficiently determine the relative bioavailability of drug formulations that have not been studied in the same randomized BE trial. The methodology developed here allows estimating formulation differences across a larger network. [ABSTRACT FROM AUTHOR]

A total of ten boars were immunized with a vaccine against GnRH and the parameters for testicular function and N-retention compared with eight intact boars and eight barrows. The immunization of boars occurred at 10, 16 and 23 weeks of age. In six pigs from each group the nitrogen retention was determined at 20/21 weeks. Blood for testosterone determination was collected daily and fat biopsies for androstenone measurement every 2nd week from week 17 to 26 in all boars. After slaughter, testes size, backfat thickness and skatole concentrations in fat were determined. The immunized boars revealed significant lower (P≤0.001) testes weights (100.5 vs. 882.6 g), plasma testosterone concentrations (0.11 vs. 2.77 ng/ml) and fat androstenone concentrations (0.06 vs. 1.31 μg/g, week 26) compared to controls. Skatole concentrations also differed significantly between immunized boars and controls (14.4 vs. 109.2 ng/g fat). The immunized boars had a significantly poorer feed conversion rate (3.82 vs. 3.01 kg/kg) and nitrogen retention (22.97 vs. 31.24 g/day) compared to boars. Barrows did not differ significantly from the immunized boars. It is concluded that an early and repeated immunization against GnRH is highly effective for a complete androstenone suppression and a considerable reduction of skatole but it also abolishes the boar-specific anabolic potential. [Copyright &y& Elsevier]

In medical research missing values often arise in the course of a data analysis. This fact constitutes a problem for different reasons, so e.g. standard methods for analyzing data lead to biased estimates and a loss of statistical power due to missing values, since those methods require complete data sets and therefore omit incomplete cases for the analyses. Furthermore missing values imply a certain loss of information for what reason the validity of results of a study with missing values has to be rated less than in a case where all data had been available. For years there are methods for replacement of missing values (Rubin, Schafer) to tackle these problems and solve them in parts. Hence in this article we want to present the existing software to handle and replace missing values on the one hand and give an outline about the available options to get information on the other hand. The methodological aspects of the replacement strategies are delineated just briefly in this article.