The Structure of Yohimbine and Other Organic Molecules by X-Ray Crystal Analysis

This thesis describes work carried out under the supervision of Professor J. M. Robertson since October 1962. It is divided into two sections. Section I describes two structures solved by the heavy-atom method. In both cases, it is the actual structure of the molecule in the chemical sense and the stereochemistry that is of interest, and for this reason refinement was terminated at a fairly early stage

Biomarker Combinations for Diagnosis and Prognosis in Multicenter Studies: Principles and Methods

Many investigators are interested in combining biomarkers to predict an outcome of interest or detect underlying disease. This endeavor is complicated by the fact that many biomarker studies involve data from multiple centers. Depending upon the relationship between center, the biomarkers, and the target of prediction, care must be taken when constructing and evaluating combinations of biomarkers. We introduce a taxonomy to describe the role of center and consider how a biomarker combination should be constructed and evaluated. We show that ignoring center, which is frequently done by clinical researchers, is often not appropriate. The limited statistical literature proposes using random intercept logistic regression models, an approach that we demonstrate is generally inadequate and may be misleading. We instead propose using fixed intercept logistic regression, which appropriately accounts for center without relying on untenable assumptions. After constructing the biomarker combination, we recommend using performance measures that account for the multicenter nature of the data, namely the center-adjusted area under the receiver operating characteristic curve. We apply these methods to data from a multicenter study of acute kidney injury after cardiac surgery. Appropriately accounting for center, both in construction and evaluation, may increase the likelihood of identifying clinically useful biomarker combinations

Developing Biomarker Combinations in Multicenter Studies via Direct Maximization and Penalization

When biomarker studies involve patients at multiple centers and the goal is to develop biomarker combinations for diagnosis, prognosis, or screening, we consider evaluating the predictive capacity of a given combination with the center-adjusted AUC (aAUC), a summary of conditional performance. Rather than using a general method to construct the biomarker combination, such as logistic regression, we propose estimating the combination by directly maximizing the aAUC. Furthermore, it may be desirable to have a biomarker combination with similar predictive capacity across centers. To that end, we allow for penalization of the variability in center-specific performance. We demonstrate good asymptotic properties of the resulting combinations. Simulations provide small-sample evidence that maximizing the aAUC can lead to combinations with greater predictive capacity than combinations constructed via logistic regression. We further illustrate the utility of constructing combinations by maximizing the aAUC while penalizing variability. We apply these methods to data from a study of acute kidney injury after cardiac surgery

Using Multilevel Outcomes to Construct and Select Biomarker Combinations for Single-level Prediction

Biomarker studies may involve a multilevel outcome, such as no, mild, or severe disease. There is often interest in predicting one particular level of the outcome due to its clinical significance. The standard approach to constructing biomarker combinations in this context involves dichotomizing the outcome and using a binary logistic regression model. We assessed whether information can be usefully gained from instead using more sophisticated regression methods. Furthermore, it is often necessary to select among several candidate biomarker combinations. One strategy involves selecting a combination on the basis of its ability to predict the outcome level of interest. We propose an algorithm that leverages the multilevel outcome to inform combination selection. We apply this algorithm to data from a study of acute kidney injury after cardiac surgery, where the kidney injury may be absent, mild, or severe. Using more sophisticated modeling approaches to construct combinations provided gains over the binary logistic regression approach in specific settings. In the examples considered, the proposed algorithm for combination selection tended to reduce the impact of bias due to selection and to provide combinations with improved performance. Methods that utilize the multilevel nature of the outcome in the construction and/or selection of biomarker combinations have the potential to yield better combinations

Linear Models for Microarray Data Analysis: Hidden Similarities and Differences

In the past several years many linear models have been proposed for analyzing two-color microarray data. As presented in the literature, many of these models appear dramatically different. However, many of these models are reformulations of the same basic approach to analyzing microarray data. This paper demonstrates the equivalence of some of these models. Attention is directed at choices in microarray data analysis that have a larger impact on the results than the choice of linear model

Combining Biomarkers by Maximizing the True Positive Rate for a Fixed False Positive Rate

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis and screening. In many applications, the true positive rate for a biomarker combination at a prespecified, clinically acceptable false positive rate is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the true positive rate while constraining the false positive rate. Theoretical results demonstrate good operating characteristics for the resulting combination. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with more traditional methods for constructing combinations

Arsenic, Cadmium, Lead, and Mercury in Sweat: A Systematic Review

Arsenic, cadmium, lead, and mercury exposures are ubiquitous. These toxic elements have no physiological benefits, engendering interest in minimizing body burden. The physiological process of sweating has long been regarded as “cleansing” and of low risk. Reports of toxicant levels in sweat were sought in Medline, Embase, Toxline, Biosis, and AMED as well as reference lists and grey literature, from inception to March 22, 2011. Of 122 records identified, 24 were included in evidence synthesis. Populations, and sweat collection methods and concentrations varied widely. In individuals with higher exposure or body burden, sweat generally exceeded plasma or urine concentrations, and dermal could match or surpass urinary daily excretion. Arsenic dermal excretion was severalfold higher in arsenic-exposed individuals than in unexposed controls. Cadmium was more concentrated in sweat than in blood plasma. Sweat lead was associated with high-molecular-weight molecules, and in an interventional study, levels were higher with endurance compared with intensive exercise. Mercury levels normalized with repeated saunas in a case report. Sweating deserves consideration for toxic element detoxification. Research including appropriately sized trials is needed to establish safe, effective therapeutic protocols