These data replicate known metabolite predictors, identify novel markers, and support the relationship between lipid oxidation and subsequent CHD.

BackgroundData generated using 'omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this paper, we consider issues relevant in the design of biomedical studies in which the goal is the discovery of a subset of features and an associated algorithm that can predict a binary outcome, such as disease status. We compare the performance of four commonly used classifiers (K-Nearest Neighbors, Prediction Analysis for Microarrays, Random Forests and Support Vector Machines) in high-dimensionality data settings. We evaluate the effects of varying levels of signal-to-noise ratio in the dataset, imbalance in class distribution and choice of metric for quantifying performance of the classifier. To guide study design, we present a summary of the key characteristics of 'omics' data profiled in several human or animal model experiments utilizing high-content mass spectrometry and multiplexed immunoassay based techniques.ResultsThe analysis of data from seven 'omics' studies revealed that the average magnitude of effect size observed in human studies was markedly lower when compared to that in animal studies. The data measured in human studies were characterized by higher biological variation and the presence of outliers. The results from simulation studies indicated that the classifier Prediction Analysis for Microarrays (PAM) had the highest power when the class conditional feature distributions were Gaussian and outcome distributions were balanced. Random Forests was optimal when feature distributions were skewed and when class distributions were unbalanced. We provide a free open-source R statistical software library (MVpower) that implements the simulation strategy proposed in this paper.ConclusionNo single classifier had optimal performance under all settings. Simulation studies provide useful guidance for the design of biomedical studies involving high-dimensionality data.

Sequentially-administered diagnostic tests used to determine the occurrence of a silent event are sometimes subject to error, leading to false positive and false negative test results. In such cases, standard methods for interval censored data do not give valid estimates of the distribution of the time to the event. We present methods for estimating the distribution of the time to the event that account for multiple types of imperfect diagnostic test, as well as differing periods at risk. We illustrate the methods with simulated data and results from a clinical trial for the prevention of mother-to-infant transmission of  in Tanzania.

Drug-induced liver injury (DILI) is the primary adverse event that results in withdrawal of drugs from the market and a frequent reason for the failure of drug candidates in development. The Liver Toxicity Biomarker Study (LTBS) is an innovative approach to investigate DILI because it compares molecular events produced in vivo by compound pairs that (a) are similar in structure and mechanism of action, (b) are associated with few or no signs of liver toxicity in preclinical studies, and (c) show marked differences in hepatotoxic potential. The LTBS is a collaborative preclinical research effort in molecular systems toxicology between the National Center for Toxicological Research and BG Medicine, Inc., and is supported by seven pharmaceutical companies and three technology providers. In phase I of the LTBS, entacapone and tolcapone were studied in rats to provide results and information that will form the foundation for the design and implementation of phase II. Molecular analysis of the rat liver and plasma samples combined with statistical analyses of the resulting datasets yielded marker analytes, illustrating the value of the broad-spectrum, molecular systems analysis approach to studying pharmacological or toxicological effects.

OBJECTIVETo examine elevated depressive symptoms and antidepressant use in relation to diabetes incidence in the Women’s Health Initiative.RESEARCH DESIGN AND METHODSA total of 161,808 postmenopausal women were followed for over an average of 7.6 years. Hazard ratios (HRs) estimating the effects of elevated depressive symptoms and antidepressant use on newly diagnosed incident diabetes were obtained using Cox proportional hazards models adjusted for known diabetes risk factors.RESULTSMultivariable-adjusted HRs indicated an increased risk of incident diabetes with elevated baseline depressive symptoms (HR 1.13 [95% CI 1.07–1.20]) and antidepressant use (1.18 [1.10–1.28]). These associations persisted through year 3 data, in which respective adjusted HRs were 1.23 (1.09–1.39) and 1.31 (1.14–1.50).CONCLUSIONSPostmenopausal women with elevated depressive symptoms who also use antidepressants have a greater risk of developing incident diabetes. In addition, longstanding elevated depressive symptoms and recent antidepressant medication use increase the risk of incident diabetes.