Comparison of Hevylite™ IgA and IgG assay with conventional techniques for the diagnosis and follow-up of plasma cell dyscrasia

Background: Heavy/light chain assay allows the characterization and quantification of immunoglobulin light chains bound to heavy chains for each Ig’k and Ig’ immunoglobulin class, discriminating between the involved/uninvolved isotypes in plasma cell dyscrasia. The Ig’k/Ig’ ratio (heavy/light chain ratio) enables to monitor the trend of monoclonal component during therapy and disease evolution. Objective: In this study, we evaluate the impact of the heavy/light chain assay in monitoring multiple myeloma patients in comparison with conventional techniques. Methods: Serum samples of 28 patients with IgG or IgA monoclonal component were collected for a mean of 109 days and analyzed. The heavy/light chain assay was compared with classical immunoglobulin quantification (Ig’Tot), serum immunofixation electrophoresis, serum protein electrophoresis, and serum-free light chains quantification. Serum samples from 30 healthy patients were used as control (polyclonal). Results: Heavy/light chain ratio and serum immunofixation electrophoresis were comparable in 86% of the cases, and free light chain ratio and heavy/light chain ratio in 71.8%. Heavy/light chain assay and Ig’Tot measurements showed a concentration-dependent agreement in monoclonal patients. The heavy/light chain assay was able to quantify the monoclonal component migrating in SPE b region: this occurred in 10% of our IgG and 50% of our IgA patients. Conclusions: The concordance scores indicate that heavy/light chain and Ig’Tot assays show differences at high monoclonal component values. The heavy/light chain ratio, serum immunofixation electrophoresis, and free light chain ratio showed partial concordance. Our study confirmed that, in the context of heavy/light chain assay, heavy/light chain Ig’k and Ig’ absolute values and heavy/light chain ratio are both important tools to monitor the presence of monoclonal component that are difficult to be identified in SPE

Efficiency of the OLS estimator in the vicinity of a spatial unit root

Previous results have indicated that the OLS estimator of the vector of regression coefficients can be nearly as efficient as the best linear unbiased estimator when the regression errors follow a spatial process with root in the vicinity of unity. Such results were derived under the assumption of a symmetric weights matrix, which simplifies the analysis considerably, but is very often not satisfied in applications. This paper provides nontrivial generalizations to the important case of nonsymmetric weights matrices. © 2011 Elsevier B.V

The Correlation Structure of Spatial Autoregressions

Least squares estimation has casually been dismissed as an inconsistent estimation method for mixed regressive, spatial autoregressive models with or without spatial correlated disturbances. Although this statement is correct for a wide class of models, we show that, in economic spatial environments where each unit can be influenced aggregately by a significant portion of units in the population, least squares estimators can be consistent. Indeed, they can even be asymptotically efficient relative to some other estimators. Their computations are easier than alternative instrumental variables and maximum likelihood approaches

Power properties of invariant tests for spatial autocorrelation in linear regression

This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005). More generally, the analysis in the paper sheds new light on how the power of tests for spatial autocorrelation is affected by the matrix of regressors and by the spatial structure. We mainly focus on the problem of residual spatial autocorrelation, in which case it is appropriate to restrict attention to the class of invariant tests, but we also consider the case when the autocorrelation is due to the presence of a spatially lagged dependent variable among the regressors. A numerical study aimed at assessing the practical relevance of the theoretical results is included. © Cambridge University Press, 2009

The correlation structure of spatial autoregressions on graphs

This paper studies the correlation structure of spatial autoregressions defined over arbitrary configurations of observational units. We derive a number of new properties of the models and provide new interpretations of some of their known properties. A little graph theory helps to clarify how the correlation between two random variables observed at two units depends on the walks connecting the two units, and allows to discuss the statistical consequences of the presence (or, more importantly in econometrics, the absence) of symmetries or regularities in the configuration of the observational units. The analysis is centered upon one-parameter models, but extensions to multi-parameter models are also considered. Keywords: exponential families; graphs; quadratic subspace; spatial autoregressions; spatial weights matrices. JEL Classification: C12, C21

Nontestability of equal weights spatial dependence

We show that any invariant test for spatial autocorrelation in a spatial error or spatial lag model with equal weights matrix has power equal to size. This result holds under the assumption of an elliptical distribution. Under Gaussianity, we also show that any test whose power is larger than its size for at least one point in the parameter space must be biased. © Cambridge University Press 2011

Testing for Spatial Autocorrelation: the Regressors that Make the Power Disappear

We show that for any sample size, any size of the test, and any weights matrix outside a small class of exceptions, there exists a positive measure set of regression spaces such that the power of the Cliff–Ord test vanishes as the autocorrelation increases in a spatial error model. This result extends to the tests that define the Gaussian power envelope of all invariant tests for residual spatial autocorrelation. In most cases, the regression spaces such that the problem occurs depend on the size of the test, but there also exist regression spaces such that the power vanishes regardless of the size. A characterization of such particularly hostile regression spaces is provided

Some correlation properties of spatial autoregressions

This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix W and the autocorrelation parameter . We provide an interpretation of the covariances between the random variables observed at two spatial units, based on a particular type of walks connecting the two units. The interpretation serves to explain a number of correlation properties of SAR(1) models, and clarifies why it is impossible to control the correlations through the specification of W