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

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

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

An Investigation of Neurological soft signs as a discriminating factor between Veterans with Post-traumatic Stress Disorder, mild Traumatic Brain Injury, and co-occurring Post-traumatic Stress Disorder and mild Traumatic Brain Injury

While multiple Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn veterans suffer from mild Traumatic Brain Injury (mTBI), Post-traumatic Stress Disorder (PTSD), and co-morbid mTBI and PTSD, there remains difficulty disentangling the specific symptoms associated with each disorder using self-report and neurocognitive assessments. We propose that neurological soft signs (NSS), which are tasks associated with general neurologic compromise, may prove useful in this regard. Based on our review of the literature we hypothesized that individuals with PTSD would present with a greater number of NSS than controls or individuals with mTBI. Further, we hypothesized a synergistic effect, such that individuals with mTBI + PTSD would present with the greatest number of NSS. To test these hypotheses, we analyzed a subset of individuals (N=238) taken from a larger study of neurocognitive functioning in veterans. Participants completed a battery of neuropsychological measures, which included the Behavioral Dyscontrol Scale (BDS), the current study’s measure of NSS. A subset of other neuropsychological measures were also included to examine the utility of NSS over and above traditional neuropsychological measures. Individuals were removed from the study if they sustained a moderate/severe TBI or did not meet validity criteria on the Green’s Word Memory Test or the Negative Impression Management subscale of the Personality Assessment Inventory. Binomial logistic and multinomial logistic regression were used to examine the ability of NSS to discriminate between the study groups, first by themselves and then after the variance explained by the traditional neuropsychological measures was accounted for. Exploratory cluster analyses were performed on neuropsychological measures and NSS to identify profiles of cognitive performance in the data set. Results indicated that individuals in the mTBI and/or PTSD group had more NSS compared to controls. Of the individual NSS items only a go/no-go task of the BDS discriminated between groups, with worse performance among individuals in the mTBI, PTSD, and mTBI + PTSD group compared to controls. In contrast, the overall BDS score and individual NSS, in general, did not discriminate between the mTBI, PTSD, and mTBI + PTSD group. Overall, the current study suggests that, when eliminating participants who do not meet validity criteria, NSS do not aid in discriminating between individuals with mTBI, PTSD, and mTBI + PTSD