Methodological Frontiers in Environmental Epidemiology

Environmental epidemiology comprises the epidemiologic study of those environmental factors that are outside the immediate control of the individual. Exposures of interest to environmental epidemiologists include air pollution, water pollution, occupational exposure to physical and chemical agents, as well as psychosocial elements of environmental concern. The main methodologic problem in environmental epidemiology is exposure assessment, a problem that extends through all of epidemiologic research but looms as a towering obstacle in environmental epidemiology. One of the most promising developments in improving exposure assessment in environmental epidemiology is to find exposure biomarkers, which could serve as built-in dosimeters that reflect the biologic footprint left behind by environmental exposures. Beyond exposure assessment, epidemiologists studying environmental exposures face the difficulty of studying small effects that may be distorted by confounding that eludes easy control. This challenge may prompt reliance on new study designs, such as two-stage designs in which exposure and disease information are collected in the first stage, and covariate information is collected on a subset of subjects in state two. While the analytic methods already available for environmental epidemiology are powerful, analytic methods for ecologic studies need further development. This workshop outlines the range of methodologic issues that environmental epidemiologists must address so that their work meets the goals set by scientists and society at large

Computer program simulates design, test, and analysis phases of sensitivity experiments

Modular program with a small main program and several specialized subroutines provides a general purpose computer program to simulate the design, test and analysis phases of sensitivity experiments. This program allows a wide range of design-response function combinations and the addition, deletion, or modification of subroutines

Estimating sufficient reductions of the predictors in abundant high-dimensional regressions

We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are possible. Simulation results are presented to support the theoretical conclusion.Comment: Published in at http://dx.doi.org/10.1214/11-AOS962 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Sparse permutation invariant covariance estimation

The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihood approach and forces sparsity by using a lasso-type penalty. We establish a rate of convergence in the Frobenius norm as both data dimension $p$ and sample size $n$ are allowed to grow, and show that the rate depends explicitly on how sparse the true concentration matrix is. We also show that a correlation-based version of the method exhibits better rates in the operator norm. We also derive a fast iterative algorithm for computing the estimator, which relies on the popular Cholesky decomposition of the inverse but produces a permutation-invariant estimator. The method is compared to other estimators on simulated data and on a real data example of tumor tissue classification using gene expression data.Comment: Published in at http://dx.doi.org/10.1214/08-EJS176 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Causation in the Presence of Weak Associations

none1siDespite their observational nature, epidemiologic studies have been used to make inductive inferences about the causes of human diseases. In this context, I mainly consider the term “cause” in its cognitive (explanatory) meaning, that is, by detecting causal factors and identifying mechanisms of diseases...openBoffetta, P.Boffetta, P

Migraine, Fibromyalgia, and Depression among People with IBS: A Prevalence Study

BACKGROUND. Case descriptions suggest IBS patients are more likely to have other disorders, including migraine, fibromyalgia, and depression. We sought to examine the prevalence of these conditions in cohorts of people with and without IBS. METHODS. The source of data was a large U.S. health plan from January 1, 1996 though June 30, 2002. We identified all people with a medical claim associated with an ICD-9 code for IBS. A non-IBS cohort was a random sample of people with an ICD-9 code for routine medical care. In the cohorts, we identified all claims for migraine, depression, and fibromyalgia. We estimated the prevalence odds ratios (PORs) of each of the three conditions using the Mantel-Haenszel method. We conducted quantitative sensitivity analyses to quantify the impact of residual confounding and in differential outcome identification. RESULTS. We identified 97,593 people in the IBS cohort, and a random sample of 27,402 people to compose the non-IBS comparison cohort. With adjustment, there was a 60% higher odds in the IBS cohort of having any one of the three disorders relative to the comparison cohort (POR 1.6, 95% CI 1.5 – 1.7). There was a 40% higher odds of depression in the IBS cohort (POR 1.4, 95% CI 1.3 – 1.4). The PORs for fibromyalgia and migraine were similar (POR for fibromyalgia 1.8, 95% CI 1.7 – 1.9; POR for migraine 1.6, 95% CI 1.4 – 1.7). Differential prevalence of an unmeasured confounder, or imperfect sensitivity or specificity of outcome detection would have impacted the observed results. CONCLUSION. People in the IBS cohort had a 40% to 80% higher prevalence odds of migraine, fibromyalgia, and depression