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

Ridge Fusion in Statistical Learning

We propose a penalized likelihood method to jointly estimate multiple precision matrices for use in quadratic discriminant analysis and model based clustering. A ridge penalty and a ridge fusion penalty are used to introduce shrinkage and promote similarity between precision matrix estimates. Block-wise coordinate descent is used for optimization, and validation likelihood is used for tuning parameter selection. Our method is applied in quadratic discriminant analysis and semi-supervised model based clustering.Comment: 24 pages and 9 tables, 3 figure

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

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

"Quantum Interference with Slits" Revisited

Marcella [arXiv:quant-ph/0703126] has presented a straightforward technique employing the Dirac formalism to calculate single- and double-slit interference patterns. He claims that no reference is made to classical optics or scattering theory and that his method therefore provides a purely quantum mechanical description of these experiments. He also presents his calculation as if no approximations are employed. We show that he implicitly makes the same approximations found in classical treatments of interference and that no new physics has been introduced. At the same time, some of the quantum mechanical arguments Marcella gives are, at best, misleading.Comment: 11 pages, 3 figure

Medicare Reimbursement for Total Joint Arthroplasty: The Driving Forces.

BACKGROUND: Total joint arthroplasty is a large and growing part of the U.S. Medicare budget, drawing attention to how much providers are paid for their services. The purpose of this study was to examine the variables that affect total joint arthroplasty reimbursement. Along with standard economic variables, we include unique health-care variables. Given the focus on value in the Affordable Care Act, the model examines the relationship of the quality of care to total joint arthroplasty reimbursement. We hoped to find that reimbursement patterns reward quality and reflect standard economic principles. METHODS: Multivariable regression was performed to identify variables that correlate with Medicare reimbursement for total joint arthroplasty. Inpatient charge or reimbursement data on Medicare reimbursements were available for 2,750 hospitals with at least 10 discharges for uncomplicated total joint arthroplasty from the Centers for Medicare & Medicaid Services (CMS) for fiscal year 2011. Reimbursement variability was examined by using the Dartmouth Atlas to group institutions into hospital referral regions and hospital service areas. Independent variables were taken from the Dartmouth Atlas, CMS, the WWAMI (Washington, Wyoming, Alaska, Montana, Idaho) Rural Health Research Center, and the United States Census. RESULTS: There were 427,207 total joint arthroplasties identified, with a weighted mean reimbursement of $14,324.84 (range,$9,103 to $38,686). Nationally, the coefficient of variation for reimbursements was 0.19. The regression model accounted for 52.5% of reimbursement variation among providers. The total joint arthroplasty provider volume (p \u3c 0.001) and patient satisfaction (p \u3c 0.001) were negatively correlated with reimbursement. Government ownership of a hospital (p \u3c 0.001) and higher Medicare costs (p \u3c 0.001) correlated positively with reimbursement. CONCLUSIONS: Medicare reimbursements for total joint arthroplasty are highly variable. Greater reimbursement was associated with lower patient volume, lower patient satisfaction, a healthier patient population, and government ownership of a hospital. As value-based reimbursement provisions of the Affordable Care Act are implemented, there will be dramatic changes in total joint arthroplasty reimbursements. To meet these changes, providers should expect qualities such as high patient volume, willingness to care for sicker patient populations, patient satisfaction, safe outcomes, and procedural demand to correlate with their reimbursement. CLINICAL RELEVANCE: Practicing orthopaedic surgeons and hospital administrators should be aware of discrepancies in inpatient reimbursement for total joint arthroplasty from Medicare. Furthermore, these discrepancies are not associated with typical economic factors. These findings warrant further investigation and collaboration between policymakers and providers to develop value-based reimbursement