Gold Phosphonates as Transfer Agents in Palladium Catalyzed Hirao Reactions

Through a cross-coupling reaction, aryl phosphonates are produced in high yields when the corresponding aryl bromides are reacted with a gold phosphorylating agent in the presence of a palladium catalyst and an appropriate ligand. To the best of our knowledge, this transformation is the first example involving the transfer of a phosphonate functional group from a gold complex to palladium that has been reported. Throughout the investigation, three gold phosphorylating agents were screened for activity towards the phosphorylation of aryl bromides. Aryl bromides with electrondonating and electron-withdrawing groups were successfully employed in the crosscoupling reactions. All cross-coupling reactions were carried out in THF at room temperature (25ºC) or in a microwave reactor (CEM Discover) at 60ºC for 30 or 60 minutes. The effects of changing reaction parameters such as time, temperature, catalyst and free ligand loading have been investigated. All aryl bromide substrates tested in the cross-coupling reactions produced phosphorylated products.

MULTIPLE MODEL EVALUATION ABSENT THE GOLD STANDARD VIA MODEL COMBINATION

We describe a method for evaluating an ensemble of predictive models given a sample of observations comprising the model predictions and the outcome event measured with error. Our formulation allows us to simultaneously estimate measurement error parameters, true outcome — aka the gold standard — and a relative weighting of the predictive scores. We describe conditions necessary to estimate the gold standard and for these estimates to be calibrated and detail how our approach is related to, but distinct from, standard model combination techniques. We apply our approach to data from a study to evaluate a collection of BRCA1/BRCA2 gene mutation prediction scores. In this example, genotype is measured with error by one or more genetic assays. We estimate true genotype for each individual in the dataset, operating characteristics of the commonly used genotyping procedures and a relative weighting of the scores. Finally, we compare the scores against the gold standard genotype and find that Mendelian scores are, on average, the more refined and better calibrated of those considered and that the comparison is sensitive to measurement error in the gold standard.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.