Comparison of different insulin resistance surrogates to predict hyperuricemia among U.S. non-diabetic adults

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.

Glomerular filtration rate (GFR) estimates facilitate detection of chronic kidney disease but require calibration of the serum creatinine assay to the laboratory that developed the equation. The 4-variable equation from the Modification of Diet in Renal Disease (MDRD) Study has been reexpressed for use with a standardized assay. To describe the performance of the revised 4-variable MDRD Study equation and compare it with the performance of the 6-variable MDRD Study and Cockcroft-Gault equations. Comparison of estimated and measured GFR. 15 clinical centers participating in a randomized, controlled trial. 1628 patients with chronic kidney disease participating in the MDRD Study. Serum creatinine levels were calibrated to an assay traceable to isotope-dilution mass spectrometry. Glomerular filtration rate was measured as urinary clearance of 125I-iothalamate. Mean measured GFR was 39.8 mL/min per 1.73 m2 (SD, 21.2). Accuracy and precision of the revised 4-variable equation were similar to those of the original 6-variable equation and better than in the Cockcroft-Gault equation, even when the latter was corrected for bias, with 90%, 91%, 60%, and 83% of estimates within 30% of measured GFR, respectively. Differences between measured and estimated GFR were greater for all equations when the estimated GFR was 60 mL/min per 1.73 m2 or greater. The MDRD Study included few patients with a GFR greater than 90 mL/min per 1.73 m2. Equations were not compared in a separate study sample. The 4-variable MDRD Study equation provides reasonably accurate GFR estimates in patients with chronic kidney disease and a measured GFR of less than 90 mL/min per 1.73 m2. By using the reexpressed MDRD Study equation with the standardized serum creatinine assay, clinical laboratories can report more accurate GFR estimates.