Correlated Binary Regression Using Orthogonalized Residuals

This paper focuses on marginal regression models for correlated binary responses when estimation of the association structure is of primary interest. A new estimating function approach based on orthogonalized residuals is proposed. This procedure allows a new representation and addresses some of the difficulties of the conditional-residual formulation of alternating logistic regressions of Carey, Zeger & Diggle (1993). The new method is illustrated with an analysis of data on impaired pulmonary function

Orthogonalized Residuals for Estimation of Marginally Specified Association Parameters in Multivariate Binary Data: Orthogonalized residuals

This paper focuses on marginal regression models for correlated binary responses when estimation of the association structure is of primary interest. A new estimating function approach based on orthogonalized residuals is proposed. A special case of the proposed procedure allows a new representation of the alternating logistic regressions method through marginal residuals. The connections between second-order generalized estimating equations, alternating logistic regressions, pseudo-likelihood and other methods are explored. Eficiency comparisons are presented, with emphasis on variable cluster size and on the role of higher-order assumptions. The new method is illustrated with an analysis of data on impaired pulmonary function

Group Testing for Case Identification with Correlated Responses

This article examines group testing procedures where units within a group (or pool) may be correlated. The expected number of tests per unit (i.e., efficiency) of hierarchical- and matrix-based procedures is derived based on a class of models of exchangeable binary random variables. The effect on efficiency of the arrangement of correlated units within pools is then examined. In general, when correlated units are arranged in the same pool, the expected number of tests per unit decreases, sometimes substantially, relative to arrangements that ignore information about correlation

Deletion Diagnostics for Alternating Logistic Regressions

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts

An R 2 statistic for fixed effects in the linear mixed model

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R2 statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R2 statistic for the linear mixed model by using only a single model. The proposed R2 statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R2 statistic arises as a 1–1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model to a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R2 statistic leads immediately to a natural definition of a partial R2 statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R2, a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated blood pressure outcomes for the study

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to “rescue” the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes

IgG Autoantibody Response against Keratinocyte Cadherins in Endemic Pemphigus Foliaceus (Fogo Selvagem)

It is well established that autoantibodies against desmoglein 3 and desmoglein 1 (Dsg1) are relevant in the pathogenesis of pemphigus vulgaris and pemphigus foliaceus, including its endemic form fogo selvagem (FS). Isolated reports have shown that in certain patients with these diseases, autoantibodies against other desmosomal cadherins and E-cadherin may also be present. The goal of this investigation was to determine whether FS patients and normal individuals living in endemic areas possess autoantibodies against other desmosomal cadherins and E-cadherin. By testing a large number of FS and endemic control sera by ELISA, we found a consistent and specific autoantibody response against Dsg1 and other keratinocyte cadherins in these individuals, which is quite different from healthy individuals from the United States (US controls). Overall, the highest correlations among the autoantibody responses tested were in the endemic controls, followed by FS patients, and lowest in the US controls. These findings suggest that multiple, perhaps cross-reactive, keratinocyte cadherins are recognized by FS patients and endemic controls.NIH [R01-AR30281, R01-AR32599, T32-AR07369

Development of an IgG4-based Classifier/Predictor of Endemic Pemphigus Foliaceus (Fogo Selvagem)

Fogo Selvagem (FS) is mediated by pathogenic, predominantly IgG4, anti-Dsg1 autoantibodies and is endemic in Limao Verde (LV), Brazil. IgG and IgG-subclass autoantibodies were tested in a sample of 214 FS patients and 261 healthy controls by Dsg1-ELISA. For model selection, the sample was randomly divided into training (50%), validation (25%) and test (25%) sets. Using the training and validation sets, IgG4 was chosen as the best predictor of FS, with index values above 6.43 classified as FS. Using the test set, IgG4 has sensitivity 92% (95% CI: 82−95%), specificity 97% (95% CI: 89−100%) and area under the curve 0.97 (95% CI: 0.94−1.00). The IgG4 positive predictive value (PPV) in LV (3% FS prevalence) was 49%. The sensitivity, specificity and PPV of IgG anti-Dsg1 were 87%, 91% and 23%, respectively. The IgG4-based classifier was validated by testing 11 FS patients before and after clinical disease and 60 Japanese pemphigus patients. It classified 21/96 normal individuals from a LV cohort as having FS serology. Based on its PPV, half of the 21 individuals may currently have preclinical FS and could develop clinical disease in the future. Identifying individuals during preclinical FS will enhance our ability to identify etiological agent(s) triggering FS

The fidelity of the ligation step determines how ends are resolved during nonhomologous end joining

Nonhomologous end joining (NHEJ) can effectively resolve chromosome breaks despite diverse end structures, but it is unclear how the steps employed for resolution are determined. We sought to address this question by analyzing cellular NHEJ of ends with systematically mispaired and damaged termini. We show NHEJ is uniquely proficient at bypassing subtle terminal mispairs and radiomimetic damage by direct ligation. Nevertheless, bypass ability varies widely, with increases in mispair severity gradually reducing bypass products from 85% to 6%. End-processing by nucleases and polymerases is increased to compensate, though paths with the fewest number of steps to generate a substrate suitable for ligation are favored. Thus, both the frequency and nature of end processing are tailored to meet the needs of the ligation step. We propose a model where the ligase organizes all steps during NHEJ within the stable paired-end complex to limit end processing and associated errors

Non–Small Cell Lung Cancer: Prognostic Importance of Positive FDG PET Findings in the Mediastinum for Patients with N0–N1 Disease at Pathologic Analysis

In non-small cell lung cancer, preoperative evaluation of the mediastinum with PET may complement the surgical and histologic findings