Conformal normal curvature and detection of masked observations in multivariate null intercept measurement error models.

Measurement errors occur very commonly in practice. After fitting the model, influence diagnostics is an important step in statistical data analysis. The most frequently used diagnostic method for measurement error models is the local influence. However, this methodology may fail to detect masked influential observations. To overcome this limitation, we propose the use of the conformal normal curvature with the forward search algorithm. The results are presented through easy to interpret plots considering different perturbation schemes. The proposed methodology is illustrated with three real data sets and one simulated data set, two of which have been previously analyzed in the literature. The third data set deals with the stability of the hygroscopic solid dosage in pharmaceutical processes to ensure the maintenance of product safety quality. In this application, the analytical mass balance is subject to measurement errors, which require attention in the modeling process and diagnostic analysis.

Fast inference for robust nonlinear mixed-effects models.

The interest for nonlinear mixed-effects models comes from application areas as pharmacokinetics, growth curves and HIV viral dynamics. However, the modeling procedure usually leads to many difficulties, as the inclusion of random effects, the estimation process and the model sensitivity to atypical or nonnormal data. The scale mixture of normal distributions include heavy-tailed models, as the Student-t, slash and contaminated normal distributions, and provide competitive alternatives to the usual models, enabling the obtention of robust estimates against outlying observations. Our proposal is to compare two estimation methods in nonlinear mixed-effects models where the random components follow a multivariate scale mixture of normal distributions. For this purpose, a Monte Carlo expectation-maximization algorithm (MCEM) and an efficient likelihood-based approximate method are developed. Results show that the approximate method is much faster and enables a fairly efficient likelihood maximization, although a slightly larger bias may be produced, especially for the fixed-effects parameters. A discussion on the robustness aspects of the proposed models are also provided. Two real nonlinear applications are discussed and a brief simulation study is presented.

Ultrastructural calibration model for proficiency testing.

Proficiency testing (PT) determines the performance of individual laboratories for specific tests or measurements and it is used to monitor the reliability of laboratories measurements. PT plays a highly valuable role as it provides objective evidence of the competence of the participant laboratories. In this paper, we propose a multivariate calibration model to assess equivalence among laboratories measurements in PT. Our method allows to deal with multivariate data, where the item under test is measured at different levels. Although intuitive, the proposed model is nonergodic, which means that the asymptotic Fisher information matrix is random. As a consequence, a detailed asymptotic analysis was carried out to establish the strategy for comparing the results of the participating laboratories. To illustrate, we apply our method to analyze the data from the Brazilian engine test group, PT program, where the power of an engine was measured by eight laboratories at several levels of rotation.

Effects of various saccharides on the masking of epitope sites and uptake in the gut of cedar allergen Cry j 1-saccharide conjugates by a naturally occurring Maillard reaction.

A major allergen of Japanese cedar, Cry j 1, was conjugated with galactomannan (M(w) of 15 kDa), dextran (M(w) of 12 kDa), xyloglucan (M(w) of 1.4 kDa), and various monosaccharides through the Maillard reaction by dry-heating in 65% relative humidity. The Cry j 1-galactomannan conjugate completely masked the epitopes of the allergen in Cry j 1. The Cry j 1-dextran conjugate also masked the epitopes of Cry j 1. The small size of oligosaccharide (xyloglucan) and various monosaccharides cannot mask the epitopes of allergen Cry j 1. This suggests that the higher molecular size of attached saccharides is important to mask sterically the epitope sites. The Cry j 1-galactomannan and Cry j 1-mannose conjugates were effectively trafficked in the gut and co-localized with immune cells, such as dendritic cells in the gut, suggesting that Cry j 1-saccharide conjugates are phagocytosed via the mannose receptor in immune cells. These results suggest that the Cry j 1-galactomannan conjugate is suitable for masking the epitope sites of Cry j 1 and trafficking to immune cells in gut lumen.

Mixture of normal distributions in multivariate null intercept measurement error model.

In this paper we propose the use of a multivariate null intercept measurement error model, where the true unobserved value of the covariate follows a mixture of two normal distributions. The proposed model is applied to a dental clinical trial presented in Hadgu and Koch (1999). A Bayesian approach is considered and a Gibbs Sampler is used to perform the computations.

Bayesian analysis of a multivariate null intercept errors-in-variables regression model.

Longitudinal data are of great interest in analysis of clinical trials. In many practical situations the covariate can not be measured precisely and a natural alternative model is the errors-in-variables regression models. In this paper we study a null intercept errors-in-variables regression model with a structure of dependency between the response variables within the same group. We apply the model to real data presented in Hadgu and Koch (Hadgu, A., Koch, G. (1999). Application of generalized estimating equations to a dental randomized clinical trial. J. Biopharmaceutical Statistics 9(1):161-178). In that study volunteers with preexisting dental plaque were randomized to two experimental mouth rinses (A and B) or a control mouth rinse with double blinding. The dental plaque index was measured for each subject in the beginning of the study and at two follow-up times, which leads to the presence of an interclass correlation. We propose the use of a Bayesian approach to model a multivariate null intercept errors-in-variables regression model to the longitudinal data. The proposed Bayesian approach accommodates the correlated measurements and incorporates the restriction that the slopes must lie in the (0, 1) interval. A Gibbs sampler is used to perform the computations.