Quantification of chlordesmethyldiazepam by liquid chromatography-tandem mass spectrometry: application to a cloxazolam bioequivalence study

A rapid, sensitive and specific LC-MS/MS method was developed and validated for quantifying chlordesmethyldiazepam (CDDZ or delorazepam), the active metabolite of cloxazolam, in human plasma. In the analytical assay, bromazepam (internal standard) and CDDZ were extracted using a liquid-liquid extraction (diethyl-ether/hexane, 80/20, v/v) procedure. The LC-MS/MS method on a RP-C18 column had an overall run time of 5.0 min and was linear (1/x weighted) over the range 0.5-50 ng/mL (R > 0.999). The between-run precision was 8.0% (1.5 ng/mL), 7.6% (9 ng/mL), 7.4% (40 ng/mL), and 10.9% at the low limit of quantification-LLOQ (0.500 ng/mL). The between-run accuracies were 0.1, -1.5, -2.7 and 8.7% for the above mentioned concentrations, respectively. All current bioanalytical method validation requirements (FDA and ANVISA) were achieved and it was applied to the bioequivalence study (Cloxazolam-test, Eurofarma Lab. Ltda and Olcadil (R)-reference, Novartis Biociencias S/A). The relative bioavailability between both formulations was assessed by calculating individual test/reference ratios for Cmax, AUClast and AUCO-inf. The pharmacokinetic profiles indicated bioequivalence since all ratios were as proposed by FDA and ANVISA. Copyright (C) 2009 John Wiley & Sons, Ltd.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Hypotheses testing for structural calibration model

The main objective of this paper is to discuss maximum likelihood inference for the comparative structural calibration model (Barnett, in Biometrics 25:129-142, 1969), which is frequently used in the problem of assessing the relative calibrations and relative accuracies of a set of p instruments, each designed to measure the same characteristic on a common group of n experimental units. We consider asymptotic tests to answer the outlined questions. The methodology is applied to a real data set and a small simulation study is presented.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.