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

Comparing semi-analytic particle tagging and hydrodynamical simulations of the Milky Way's stellar halo

Particle tagging is an efficient, but approximate, technique for using cosmological N-body simulations to model the phase-space evolution of the stellar populations predicted, for example, by a semi-analytic model of galaxy formation. We test the technique developed by Cooper et al. (which we call STINGS here) by comparing particle tags with stars in a smooth particle hydrodynamic (SPH) simulation. We focus on the spherically averaged density profile of stars accreted from satellite galaxies in a Milky Way (MW)-like system. The stellar profile in the SPH simulation can be recovered accurately by tagging dark matter (DM) particles in the same simulation according to a prescription based on the rank order of particle binding energy. Applying the same prescription to an N-body version of this simulation produces a density profile differing from that of the SPH simulation by ≲10 per cent on average between 1 and 200 kpc. This confirms that particle tagging can provide a faithful and robust approximation to a self-consistent hydrodynamical simulation in this regime (in contradiction to previous claims in the literature). We find only one systematic effect, likely due to the collisionless approximation, namely that massive satellites in the SPH simulation are disrupted somewhat earlier than their collisionless counterparts. In most cases, this makes remarkably little difference to the spherically averaged distribution of their stellar debris. We conclude that, for galaxy formation models that do not predict strong baryonic effects on the present-day DM distribution of MW-like galaxies or their satellites, differences in stellar halo predictions associated with the treatment of star formation and feedback are much more important than those associated with the dynamical limitations of collisionless particle tagging

The Australian Charter of Employment Rights: The missing dimensions

Just prior to the 2007 General Election, a group of labour lawyers and economists, broadly sympathetic to the Labor Party, produced a Charter of Employment Rights. This article examines the Charter's proposals and its underlying framework, and suggests significant aspects of work and labour have been omitted. It contends that the Charter would have been improved if it had not retained an artificially stretched definition of workers as employees, in which the only relationship worthy of inclusion in a Charter is that between the direct employer and employee. The framework and language of the Charter convey a paternalistic approach and an outdated focus on industrial labour, while ignoring aspects of the emerging global system of work linked to the concept of occupation

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

Narrowband UVB phototherapy for clinically isolated syndrome: A trial to deliver the benefits of Vitamin D and other UVB-Induced molecules

Low vitamin D and insufficient sun exposure are additive independent risk factors for the development of multiple sclerosis (MS). The usual measure of vitamin D status, serum 25-hydroxy vitamin D [25(OH)D], is also a marker of recent exposure to the UVB rays of sunshine. The main evidence for a protective effect for MS development of higher 25(OH)D comes from observational studies, but this study design cannot separate out whether 25(OH)D is acting as a marker of vitamin D status, sun exposure, or both. In light of a lack of definitive outcomes in MS patients after trials of vitamin D supplementation and the ability of narrowband UVB to induce vitamin D, as well as other immune-regulatory molecules in skin, the Phototherapy for Clinically Isolated Syndrome (PhoCIS) trial was established to investigate the benefits of narrowband UVB, in addition to supplemented vitamin D, on MS development in individuals with Clinically Isolated Syndrome. We propose that the PhoCIS trial provides a fresh approach to re-defining the reported associations of 25(OH)D levels with MS development and progression

Phonons and specific heat of linear dense phases of atoms physisorbed in the grooves of carbon nanotube bundles

The vibrational properties (phonons) of a one-dimensional periodic phase of atoms physisorbed in the external groove of the carbon nanotube bundle are studied. Analytical expressions for the phonon dispersion relations are derived. The derived expressions are applied to Xe, Kr and Ar adsorbates. The specific heat pertaining to dense phases of these adsorbates is calculated.Comment: 4 PS figure

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

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)

A WARNING ABOUT USING PREDICTED VALUES TO ESTIMATE DESCRIPTIVE MEASURES

In a recent article in the Journal, Ogburn et al. highlighted the issues with using predicted values when estimating associations or effects. While the authors cautioned against using predicted values to estimate associations or effects, they noted that predictions can be useful for descriptive purposes. In this work, we highlight the issues with using individual-level predicted values to estimate population-level descriptive parameter

Standardizing Discrete-Time Hazard Ratios with a Disease Risk Score

The disease risk score (DRS) is a summary score that is a function of a potentially large set of covariates. The DRS can be used to control for confounding by the covariates that went into estimation of the DRS and obtain a standardized estimate of an exposure's effect on disease. However, to date, literature on the DRS has not addressed analyses that focus on estimation of survival or hazard functions, which are common in epidemiologic analyses of cohort data. Here, we propose a method for standardization of hazard ratios using the DRS in longitudinal analyses of the association between a binary exposure and an outcome. This approach to handling a potentially large set of covariates through a model-based approach to standardization may provide a useful tool for cohort analyses of hazard ratios and may be particularly well-suited to settings where an exposure propensity score is difficult to model. Simulations are used in this paper to illustrate the approach, and an empirical example is provided