Medicine and Health Sciences Commons^™

Finding Recurrent Regions Of Copy Number Variation: A Review, Oscar M. Rueda, Ramon Diaz-Uriarte

Ramon Diaz-Uriarte

Copy number alterations (CNA) in genomic DNA are linked to a variety of human diseases. Although many methods have been developed to analyze data from a single subject, disease-critical genes are more likely to be found in regions that are common or recurrent among diseased subjects. Unfortunately, finding recurrent CNA regions remains a challenge. We review existing methods for the identification of recurrent CNA regions. Methods differ in their working definition of ``recurrent region'', the type of input data, the statistical and computational methods used to identify recurrence, and the biological considerations they incorporate (which play a role in the …

Regressing Scalar Outcomes On Image Predictors Via Functional Principal Component Regression, Philip T. Reiss

Philip T. Reiss

No abstract provided.

Survival Unchanged Five Months After Implementing The 2005 Aha Cpr And Ecc Guidelines For Out-Of-Hospital Cardiac Arrest., Blair L. Bigham, Kent M. Koprowicz, John Stouffer, Tom P. Aufderheide, Stuart Donn, Judy Powell, Dan Davis, Sarah Nafziger, Brian Suffoletto, Ahamed Idris, Mike Helbock, Laurie J. Morrison

Kent M Koprowicz

Introduction: To improve survival from out of hospital cardiac arrest (OHCA), the American Heart Association released guidelines in 2005. We examined the effect of these guidelines on survival in the Resuscitation Outcomes Consortium (ROC) Epistry – Cardiac Arrest. We hypothesized that survival would increase after guideline implementation. Methods: 174 EMS agencies from 8 of the 10 ROC sites were surveyed to determine 2005 AHA guideline implementation, or crossover, date. Two sites with 2005 compatible treatment algorithms prior to guideline release were not included. Patients with OHCA secondary to a non cardiac cause, EMS witnessed events, patients <18 years>old, and patients with …

Composite Endpoint Analysis For Assessing Surrogacy With Censored Data, Debashis Ghosh

Debashis Ghosh

Background: There is great interest in the development of surrogate endpoints using new technologies in medical research. The promise of such endpoints is that they would allow for faster completion of clinical trials and would be potentially cost-effective.

Purpose: In determining surrogacy, it is important to distinguish the roles of surrogate from the true endpoint. The latter should be thought of as the gold standard. We discuss a framework in which the utility of a surrogate endpoint is based on whether or not as part of a composite endpoint, it yields treatment effects that associate with that on the true …

Worldwide Variation In The Doubling Time Of Alzheimer's Disease Incidence Rates, Kathryn Ziegler-Graham, Ron Brookmeyer, Elizabeth Johnson, H. Michael Arrighi

Ron Brookmeyer

Background The doubling time is the number of chronological years for the age-specific incidence rate to double in magnitude. Doubling times describe the rate of increase of the risk of Alzheimer's disease (AD) with advancing age. Estimates of doubling times of AD assist in understanding disease etiology and forecasting future disease prevalence. The objective of this study was to investigate regional and gender differences in the doubling of AD age-specific incidence rates.

Methods We identified all studies in the peer review literature that reported age-specific incidence rates for AD. We modeled the logarithm of the incidence rate as a linear …

Simultaneous Confidence Bands For The Coefficient Function In Functional Regression, Philip T. Reiss

Philip T. Reiss

No abstract provided.

Inferring Group Differences In Brain Connectivity From Functional Magnetic Resonance Images, Philip T. Reiss

Philip T. Reiss

No abstract provided.

Reliability Of Functional Connectivity Networks: How Can We Assess It?, Philip T. Reiss

Philip T. Reiss

No abstract provided.

On Correcting The Overestimation Of The Permutation Based False Discovery Rate Estimator., Shuo Jiao, Shunpu Zhang

Shuo Jiao

Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR), which is defined as the expected percentage of the number of false positive genes among the claimed significant genes. As a consequence, the accuracy of the FDR estimators will

be important for correctly controlling FDR. Xie et al. found that the standard permutation method of estimating FDR is biased and proposed to delete the predicted differentially expressed (DE) genes in the estimation of FDR for one-sample comparison. However, we notice that the formula of the FDR used …

Bayesian Identification, Selection And Estimation Of Functions In High-Dimensional Additive Models, Anastasios Panagiotelis, Michael Smith

Michael Stanley Smith

In this paper we propose an approach to both estimate and select unknown smooth functions in an additive model with potentially many functions. Each function is written as a linear combination of basis terms, with coefficients regularized by a proper linearly constrained Gaussian prior. Given any potentially rank deficient prior precision matrix, we show how to derive linear constraints so that the corresponding effect is identified in the additive model. This allows for the use of a wide range of bases and precision matrices in priors for regularization. By introducing indicator variables, each constrained Gaussian prior is augmented with a …

Microproteomics: Analysis Of Protein Diversity In Small Samples, Howard B. Gutstein, Jeffrey S. Morris, Suresh P. Annangudi, Jonathan V. Sweedler

Jeffrey S. Morris

Proteomics, the large-scale study of protein expression in organisms, offers the potential to evaluate global changes in protein expression and their post-translational modifications that take place in response to normal or pathological stimuli. One challenge has been the requirement for substantial amounts of tissue in order to perform comprehensive proteomic characterization. In heterogeneous tissues, such as brain, this has limited the application of proteomic methodologies. Efforts to adapt standard methods of tissue sampling, protein extraction, arraying, and identification are reviewed, with an emphasis on those appropriate to smaller samples ranging in size from several microliters down to single cells. The …

Comment On Global Dynamics Of Biological Systems, Radhakrishnan Nagarajan

Radhakrishnan Nagarajan

No abstract provided.

The T-Mixture Model Approach For Detecting Differentially Expressed Genes In Microarrays, Shuo Jiao, Shunpu Zhang

Shuo Jiao

The finite mixture model approach has attracted much attention in analyzing microarray data due to its robustness to the excessive variability which is common in the microarray data. Pan (2003) proposed to use the normal mixture model method (MMM) to estimate the distribution of a test statistic and its null distribution. However, considering the fact that the test statistic is often of t-type, our studies find that the rejection region from MMM is often significantly larger than the correct rejection region, resulting an inflated type I error. This motivates us to propose the t-mixture model (TMM) approach. In this paper, …

Software For Fitting Hierarchical Spatial Functional Models, Veera Baladandayuthapani

Veera Baladandayuthapani

No abstract provided.

Direct Effect Models, Mark J. Van Der Laan, Maya L. Petersen

Maya Petersen

The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is not mediated by an intermediate variable (the direct effect of the treatment) is often relevant to mechanistic understanding and to the design of clinical and public health interventions. Robins, Greenland and Pearl develop counterfactual definitions for two types of direct effects, natural and controlled, and discuss assumptions, beyond those of sequential randomization, required for the identifiability of natural direct effects. Building on their earlier work and that of others, this article …

Characterizing Pharmacy And Medical Claims For A Private Insurance Polypharmacy Population, Brian W. Bresnahan, Kent M. Koprowicz, Sanchita Roy Choudhury, Louis P. Garrison, Ed Wong

Kent M Koprowicz

Objectives: To describe and characterize a group of private insurance members taking multiple medications over a one-year period. Methods: Persons were selected for this polypharmacy analysis if they had at least five unique maintenance prescriptions in their pharmacy claims records for the period of January-March 2005, based on a customized list of chronic medications. The full set of pharmacy and medical claims for these members were evaluated for a twelve month period, October 2004 to September 2005. Standard descriptive statistics were calculated to characterize the population. Logistic regression models were used to assess the association of pharmacy claims and “safety …

Multiple Testing Procedures Under Confounding, Debashis Ghosh

Debashis Ghosh

While multiple testing procedures have been the focus of much statistical research, an important facet of the problem is how to deal with possible confounding. Procedures have been developed by authors in genetics and statistics. In this chapter, we relate these proposals. We propose two new multiple testing approaches within this framework. The first combines sensitivity analysis methods with false discovery rate estimation procedures. The second involves construction of shrinkage estimators that utilize the mixture model for multiple testing. The procedures are illustrated with applications to a gene expression profiling experiment in prostate cancer.

Joint Variable Selection And Classification With Immunohistochemical Data, Debashis Ghosh, Ratna Chakrabarti

Debashis Ghosh

To determine if candidate cancer biomarkers have utility in a clinical setting, validation using immunohistochemical methods is typically done. Most analyses of such data have not incorporated the multivariate nature of the staining profiles. In this article, we consider modelling such data using recently developed ideas from the machine learning community. In particular, we consider the joint goals of feature selection and classification. We develop esti- mation procedures for the analysis of immunohistochemical profiles using the least absolute selection and shrinkage operator. These lead to novel and flexible models and algorithms for the analysis of compositional data. The techniques are …

An Improved Model Averaging Scheme For Logistic Regression, Debashis Ghosh, Zheng Yuan

Debashis Ghosh

Recently, penalized regression methods have attracted much attention in the statistical literature. In this article, we argue that such methods can be improved for the purposes of prediction by utilizing model averaging ideas. We propose a new algorithm that combines penalized regression with model averaging for improved prediction. We also discuss the issue of model selection versus model averaging and propose a diagnostic based on the notion of generalized degrees of freedom. The proposed methods are studied using both simulated and real data.

Multinomial Logistic Regression: An Application To Estimating Performance Of A Multiple Screening Test For Bowel Cancer When Negatives Are Unverified., Chris Lloyd, Don Frommer

Chris J. Lloyd

This paper describes a method of estimating the performance of a multiple screening test where those who test negative do not have their true disease status determined. The methodology is motivated by a dataset on 49,927 subjects who were given K=6 binary tests for bowel cancer. A complicating factor is that individuals may have polyps present in the bowel, a condition that the screening test is not designed to detect but which may be worth diagnosing. The methodology is based on a multinomial logit model for Pr(S|R_6), the probability distribution of patient status S (healthy, polyps or diseased) conditional on …

A New Exact And More Powerful Unconditional Test Of No Treatment Effect From Binary Matched Pairs, Chris Lloyd

Chris J. Lloyd

We consider the problem of testing for a difference in the probability of success from matched binary pairs. Starting with three standard inexact tests, the nuisance parameter is first estimated and then the residual dependence is eliminated by maximisation, producing what I call an E+M P-value. The E+M P-value based on McNemar's statistic is shown numerically to dominate previous suggestions, including partially maximised P-values as described in Berger and Sidik (2003). The latter method however may have computational advantages for large samples.

Long-Term Consequences Of The Delay Between Virologic Failure Of Highly Active Antiretroviral Therapy And Regimen Modification, Maya L. Petersen, Mark J. Van Der Laan, Napravnik Sonia, Joseph J. Eron, Richard G. Moore, Steven G. Deeks

Maya Petersen

Objectives: Current treatment guidelines recommend immediate modification of antiretroviral therapy in HIV-infected individuals with incomplete viral suppression. These recommendations have not been tested in observational studies or large randomized trials. We evaluated the consequences of delayed modification following virologic failure. Design/methods: We used prospective data from two clinical cohorts to estimate the effect of time until regimen modification following first regimen failure on all-cause mortality. The impact of regimen type was also assessed. As the effect of delayed switching can be confounded if patients with a poor prognosis modify therapy earlier than those with a good prognosis, we used a …

Functional Generalized Linear Models With Applications To Neuroimaging, Philip T. Reiss, R. Todd Ogden

Philip T. Reiss

No abstract provided.