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

Evidence-based Target Recall Rates for Screening Mammography 1

PURPOSE: To retrospectively identify target recall rates for screening mammography on the basis of how sensitivity shifts with recall rate. MATERIALS AND METHODS: The study group included 1 872 687 subsequent and 171 104 first screening mammograms from 1996 to 2001 from 172 and 139 facilities, respectively, in six sites of the Breast Cancer Surveillance Consortium. Institutional review board (IRB) approval was obtained from each site. Informed consent requirements of the IRBs were followed. The study was HIPAA compliant. Recall rate was defined as the percentage of screening studies for which further work-up was recommended by the radiologist. Sensitivity was defined as the proportion of cancers that were detected at screening mammography. Piecewise linear regression was used to model sensitivity as a function of recall rate. This model allows detection of critical recall rates in which significant changes (shifts) occurred in the rates that sensitivity increased with increasing recall rate. Rates were interpreted as number of additional work-ups per additional cancer detected (AW/ACD) or, in other words, the estimated number of additional women needed to be recalled at a given rate to detect one additional cancer. RESULTS: For first mammograms, a single shift in the estimated AW/ACD rate occurred at a recall rate of 10.0%, with the rate jumping dramatically from 35 to 172. For subsequent mammograms, four shifts were identified. At a recall rate of 6.7%, the estimated AW/ACD increased from 80 to 132, which rendered it the highest desirable target recall rate. At a recall rate of 12.3%, the estimated AW/ACD was 304, which suggests little benefit for any higher recall rate. CONCLUSION: Recall rates of 10.0% for first and 6.7% for subsequent mammograms are recommended targets on the basis of their AW/ACD rates (less than 100)

Kappa statistic for clustered dichotomous responses from physicians and patients

The bootstrap method for estimating the standard error of the kappa statistic in the presence of clustered data is evaluated. Such data arise, for example, in assessing agreement between physicians and their patients regarding their understanding of the physician-patient interaction and discussions. We propose a computationally efficient procedure for generating correlated dichotomous responses for physicians and assigned patients for simulation studies. The simulation result demonstrates that the proposed bootstrap method produces better estimate of the standard error and better coverage performance compared to the asymptotic standard error estimate that ignores dependence among patients within physicians with at least a moderately large number of clusters. An example of an application to a coronary heart disease prevention study is presented

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

Elevated Airway Purines in COPD

Adenosine and related purines have established roles in inflammation, and elevated airway concentrations are predicted in patients with COPD. However, accurate airway surface purine measurements can be confounded by stimulation of purine release during collection of typical respiratory samples

The IgM Anti-Desmoglein 1 Response Distinguishes Brazilian Pemphigus Foliaceus (Fogo Selvagem) from Other Forms of Pemphigus

Fogo selvagem (FS) and pemphigus foliaceus (PF) possess pathogenic IgG anti-desmoglein 1-(Dsg1) autoantibodies. Although PF occurs sporadically, FS is endemic in Limao Verde (LV), Brazil (3.4% prevalence). IgM anti-Dsg1 were detected in 58% FS LV patients (n=31), 19% of FS patients from Hospital-Campo Grande (n=57), 19% from Hospital-Goiania (n=42), 12% from Hospital-Sao Paulo (n=56), 10% of PF patients from United States (n=20), and 0% of PF patients from Japan (n=20). Pemphigus vulgaris (n=40, USA and Japan), bullous pemphigoid (n=40, USA), and healthy donors (n=55, USA) showed negligible percentages of positive sera. High percentages of positive IgM anti-Dsg1 were found in healthy donors from four rural Amerindian populations (42% of 243) as compared with urban donors (14% of 81; P<0.001). More than 50% of healthy donors from LV (n = 99, age 5-20 years) possess IgM anti-Dsg1 across ages, whereas IgG-anti-Dsg1 was detected in 2.9% (age 5-10 years), 7.3% (age 11-15 years), and 29% of donors above age 16. IgM anti-Dsg1 epitopes are Ca2+and carbohydrate-independent. We propose that IgM anti-Dsg1 are common in FS patients in their native environment and uncommon in other pemphigus phenotypes and in FS patients who migrate to urban hospitals. Recurrent environmental antigenic exposure may lead to IgM and IgG responses that trigger FS