Assessing Exposure-Response Trends Using the Disease Risk Score

Standardization by a disease risk score (DRS) may be preferable to weighting on the exposure propensity score if the exposure is difficult to model (1), relatively novel (i.e., newly emerging or rapidly-evolving), or extremely rare (2, 3). For exposures with more than two levels, methods are lacking for a DRS-based approach. We present an approach to estimate trends in standardized risk ratios (RRs) based on a regression model that uses a DRS

Active Brownian Particles. From Individual to Collective Stochastic Dynamics

We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically powered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active particles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte

Decreased Susceptibility of Marginal Odds Ratios to Finite-sample Bias

Parameters representing adjusted treatment effects may be defined marginally or conditionally on covariates. The choice between a marginal or covariate-conditional parameter should be driven by the study question. However, an unappreciated benefit of marginal estimators is a reduction in susceptibility to finite-sample bias relative to the unpenalized maximum likelihood estimator of the covariate-conditional odds ratio (OR). Using simulation, we compare the finite-sample bias of different marginal and conditional estimators of the OR. We simulated a logistic model to have 15 events per parameter and two events per parameter. We estimated the covariate-conditional OR by maximum likelihood with and without Firth's penalization. We used three estimators of the marginal OR: g-computation, inverse probability of treatment weighting, and augmented inverse probability of treatment weighting. At 15 events per parameter, as expected, all estimators were effectively unbiased. At two events per parameter, the unpenalized covariate-conditional estimator was notably biased but penalized covariate-conditional and marginal estimators exhibited minimal bias

Amplification of Bias Due to Exposure Measurement Error

Observational epidemiologic studies typically face challenges of exposure measurement error and confounding. Consider an observational study of the association between a continuous exposure and an outcome, where the exposure variable of primary interest suffers from classical measurement error (i.e., the measured exposures are distributed around the true exposure with independent error). In the absence of exposure measurement error, it is widely recognized that one should control for confounders of the association of interest to obtain an unbiased estimate of the effect of that exposure on the outcome of interest. However, here we show that, in the presence of classical exposure measurement error, the net bias in an estimate of the association of interest may increase upon adjustment for confounders. We offer an analytical expression for calculating the change in net bias in an estimate of the association of interest upon adjustment for a confounder in the presence of classical exposure measurement error, and we illustrate this problem using simulations

Relativistic Calculation of the Meson Spectrum: a Fully Covariant Treatment Versus Standard Treatments

A large number of treatments of the meson spectrum have been tried that consider mesons as quark - anti quark bound states. Recently, we used relativistic quantum "constraint" mechanics to introduce a fully covariant treatment defined by two coupled Dirac equations. For field-theoretic interactions, this procedure functions as a "quantum mechanical transform of Bethe-Salpeter equation". Here, we test its spectral fits against those provided by an assortment of models: Wisconsin model, Iowa State model, Brayshaw model, and the popular semi-relativistic treatment of Godfrey and Isgur. We find that the fit provided by the two-body Dirac model for the entire meson spectrum competes with the best fits to partial spectra provided by the others and does so with the smallest number of interaction functions without additional cutoff parameters necessary to make other approaches numerically tractable. We discuss the distinguishing features of our model that may account for the relative overall success of its fits. Note especially that in our approach for QCD, the resulting pion mass and associated Goldstone behavior depend sensitively on the preservation of relativistic couplings that are crucial for its success when solved nonperturbatively for the analogous two-body bound-states of QED.Comment: 75 pages, 6 figures, revised content

Surface Normal Deconvolution: Photometric Stereo for Optically Thick Translucent Objects

Computer Vision – ECCV 2014 13th European Conference, Zurich, Switzerland, September 6-12, 2014,This paper presents a photometric stereo method that works for optically thick translucent objects exhibiting subsurface scattering. Our method is built upon the previous studies showing that subsurface scattering is approximated as convolution with a blurring kernel. We extend this observation and show that the original surface normal convolved with the scattering kernel corresponds to the blurred surface normal that can be obtained by a conventional photometric stereo technique. Based on this observation, we cast the photometric stereo problem for optically thick translucent objects as a deconvolution problem, and develop a method to recover accurate surface normals. Experimental results of both synthetic and real-world scenes show the effectiveness of the proposed method

Estimating the impact of changes to occupational standards for silica exposure on lung cancer mortality

Background: Respiratory exposure to silica is associated with the risk of death owing to malignant and nonmalignant disease. 2.3 million US workers are exposed to silica. Occupational exposure limits for silica are derived from a number of lines of evidence, including observational studies. Observational studies may be subject to healthy worker survivor bias, which could result in underestimates of silica's impact on worker mortality and, in turn, bias risk estimates for occupational exposure limits. Methods: Using data on 65, 999 workers pooled across multiple industries, we estimate the impacts of several hypothetical occupational exposure limits on silica exposure on lung cancer and all-cause mortality. We use the parametric g-formula, which can account for healthy worker survivor bias. Results: Assuming we could eliminate occupational exposure, we estimate that there would be 20.7 fewer deaths per 1, 000 workers in our pooled study by age 80 (95% confidence interval = 14.5, 26.8), including 3.91 fewer deaths owing to lung cancer (95% CI = 1.53, 6.30). Less restrictive interventions demonstrated smaller but still substantial risk reductions. Conclusions: Our results suggest that occupational exposure limits for silica can be further strengthened to reduce silica-associated mortality and illustrate how current risk analysis for occupational limits can be improved

Reducing Bias Due to Exposure Measurement Error Using Disease Risk Scores

Suppose that an investigator wants to estimate an association between a continuous exposure variable and an outcome, adjusting for a set of confounders. If the exposure variable suffers classical measurement error, in which the measured exposures are distributed with independent error around the true exposure, then an estimate of the covariate-Adjusted exposure-outcome association may be biased. We propose an approach to estimate a marginal exposure-outcome association in the setting of classical exposure measurement error using a disease score-based approach to standardization to the exposed sample. First, we show that the proposed marginal estimate of the exposure-outcome association will suffer less bias due to classical measurement error than the covariate-conditional estimate of association when the covariates are predictors of exposure. Second, we show that if an exposure validation study is available with which to assess exposure measurement error, then the proposed marginal estimate of the exposure-outcome association can be corrected for measurement error more efficiently than the covariate-conditional estimate of association. We illustrate both of these points using simulations and an empirical example using data from the Orinda Longitudinal Study of Myopia (California, 1989-2001)

Marginal Structural Models for Risk or Prevalence Ratios for a Point Exposure Using a Disease Risk Score

The disease risk score is a summary score that can be used to control for confounding with a potentially large set of covariates. While less widely used than the exposure propensity score, the disease risk score approach might be useful for novel or unusual exposures, when treatment indications or exposure patterns are rapidly changing, or when more is known about the nature of how covariates cause disease than is known about factors influencing propensity for the exposure of interest. Focusing on the simple case of a binary point exposure, we describe a marginal structural model for estimation of risk (or prevalence) ratios. The proposed model incorporates the disease risk score as an offset in a regression model, and it yields an estimate of a standardized risk ratio where the target population is the exposed group. Simulations are used to illustrate the approach, and an empirical example is provided. Confounder control based on the proposed method might be a useful alternative to approaches based on the exposure propensity score, or as a complement to them

The Science of Sungrazers, Sunskirters, and Other Near-Sun Comets

This review addresses our current understanding of comets that venture close to the Sun, and are hence exposed to much more extreme conditions than comets that are typically studied from Earth. The extreme solar heating and plasma environments that these objects encounter change many aspects of their behaviour, thus yielding valuable information on both the comets themselves that complements other data we have on primitive solar system bodies, as well as on the near-solar environment which they traverse. We propose clear definitions for these comets: We use the term near-Sun comets to encompass all objects that pass sunward of the perihelion distance of planet Mercury (0.307 AU). Sunskirters are defined as objects that pass within 33 solar radii of the Sun’s centre, equal to half of Mercury’s perihelion distance, and the commonly-used phrase sungrazers to be objects that reach perihelion within 3.45 solar radii, i.e. the fluid Roche limit. Finally, comets with orbits that intersect the solar photosphere are termed sundivers. We summarize past studies of these objects, as well as the instruments and facilities used to study them, including space-based platforms that have led to a recent revolution in the quantity and quality of relevant observations. Relevant comet populations are described, including the Kreutz, Marsden, Kracht, and Meyer groups, near-Sun asteroids, and a brief discussion of their origins. The importance of light curves and the clues they provide on cometary composition are emphasized, together with what information has been gleaned about nucleus parameters, including the sizes and masses of objects and their families, and their tensile strengths. The physical processes occurring at these objects are considered in some detail, including the disruption of nuclei, sublimation, and ionisation, and we consider the mass, momentum, and energy loss of comets in the corona and those that venture to lower altitudes. The different components of comae and tails are described, including dust, neutral and ionised gases, their chemical reactions, and their contributions to the near-Sun environment. Comet-solar wind interactions are discussed, including the use of comets as probes of solar wind and coronal conditions in their vicinities. We address the relevance of work on comets near the Sun to similar objects orbiting other stars, and conclude with a discussion of future directions for the field and the planned ground- and space-based facilities that will allow us to address those science topics