Homogeneity pursuit and variable selection in regression models for multivariate abundance data.

When building regression models for multivariate abundance data in ecology, it is important to allow for the fact that the species are correlated with each other. Moreover, there is often evidence species exhibit some degree of homogeneity in their responses to each environmental predictor, and that most species are informed by only a subset of predictors. We propose a generalized estimating equation (GEE) approach for simultaneous homogeneity pursuit (ie, grouping species with similar coefficient values while allowing differing groups for different covariates) and variable selection in regression models for multivariate abundance data.

C-reactive protein and serum creatinine, but not haemoglobin A1c, are independent predictors of coronary heart disease risk in non-diabetic Chinese.

Background: In western populations, high-sensitivity C-reactive protein (hsCRP), and to a lesser degree serum creatinine and haemoglobin A1c, predict risk of coronary heart disease (CHD). However, data on Asian populations that are increasingly affected by CHD are sparse and it is not clear whether these biomarkers can be used to improve CHD risk classification.

Design And Methods: We conducted a nested case-control study within the Singapore Chinese Health Study cohort, with incident 'hard' CHD (myocardial infarction or CHD death) as an outcome.

"Magnitude-based inference": a statistical review.

Purpose: We consider "magnitude-based inference" and its interpretation by examining in detail its use in the problem of comparing two means.

Methods: We extract from the spreadsheets, which are provided to users of the analysis (http://www.sportsci.

Fitting and interpreting occupancy models.

We show that occupancy models are more difficult to fit than is generally appreciated because the estimating equations often have multiple solutions, including boundary estimates which produce fitted probabilities of zero or one. The estimates are unstable when the data are sparse, making them difficult to interpret, and, even in ideal situations, highly variable. As a consequence, making accurate inference is difficult.

Designing 2-phase prevalence studies in the absence of a "gold standard" test.

A population survey for estimating prevalence is challenging when a disease or condition is difficult to diagnose. If clinical diagnosis is expensive, a 2-phase study, in which less expensive but less accurate tests are administered to all study subjects in the first phase (screening phase) and a more accurate but expensive or time-consuming test is administered to only a subset of the subjects in the second phase, is an attractive approach. Published research has discussed ways of maximizing precision of the prevalence estimate from a 2-phase study with a "gold standard" second-phase test.

Statistical modeling of a ligand knowledge base.

A range of different statistical models has been fitted to experimental data for the Tolman electronic parameter (TEP) based on a large set of calculated descriptors in a prototype ligand knowledge base (LKB) of phosphorus(III) donor ligands. The models have been fitted by ordinary least squares using subsets of descriptors, principal component regression, and partial least squares which use variables derived from the complete set of descriptors, least angle regression, and the least absolute shrinkage and selection operator. None of these methods is robust against outliers, so we also applied a robust estimation procedure to the linear regression model.