Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Biostatistics

PDF

COBRA

2007

Keyword
Publication

Articles 31 - 41 of 41

Full-Text Articles in Physical Sciences and Mathematics

Racial Disparities In Mortality Risks In A Sample Of The U.S. Medicare Population, Yijie Zhou, Francesca Dominici, Thomas A. Louis May 2007

Racial Disparities In Mortality Risks In A Sample Of The U.S. Medicare Population, Yijie Zhou, Francesca Dominici, Thomas A. Louis

Johns Hopkins University, Dept. of Biostatistics Working Papers

Racial disparities in mortality risks adjusted by socioeconomic status (SES) are not well understood. To add to the understanding of racial disparities, we construct and analyze a data set that links, at individual and zip code levels, three government databases: Medicare, Medicare Current Beneficiary Survey and U.S. Census. Our study population includes more than 4 million Medicare enrollees residing in 2095 zip codes in the Northeast region of U.S. We develop hierarchical models to estimate Black-White disparity in risk of death, adjusted by both individual-level and zip codelevel income. We define population-level attributable risk (AR), relative attributable risk (RAR) and …


Adjusting For Covariates In Studies Of Diagnostic, Screening, Or Prognostic Markers: An Old Concept In A New Setting, Holly Janes, Margaret Pepe May 2007

Adjusting For Covariates In Studies Of Diagnostic, Screening, Or Prognostic Markers: An Old Concept In A New Setting, Holly Janes, Margaret Pepe

UW Biostatistics Working Paper Series

The concept of covariate adjustment is well established in therapeutic and etiologic studies. However, it has received little attention in the growing area of medical research devoted to the development of markers for disease diagnosis, screening, or prognosis, where classification accuracy, rather than association, is of primary interest. In this paper, we demonstrate the need for covariate adjustment in studies of classification accuracy, discuss methods for adjusting for covariates, and distinguish covariate adjustment from several other related but fundamentally different uses for covariates. We draw analogies and contrasts throughout with studies of association.


A Case Study In Pharmacologic Imaging Using Principal Curves In Single Photon Emission Computed Tomography, Brian S. Caffo, Ciprian M. Crainiceanu, Lijuan Deng, Craig W. Hendrix May 2007

A Case Study In Pharmacologic Imaging Using Principal Curves In Single Photon Emission Computed Tomography, Brian S. Caffo, Ciprian M. Crainiceanu, Lijuan Deng, Craig W. Hendrix

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this manuscript we are concerned with functional imaging of the colon to assess the kinetics of a microbicide lubricant. The overarching goal is to understand the distribution of the lubricant in the colon. Such information is crucial for understanding the potential impact of the microbicide on HIV viral transmission. The experiment was conducted by imaging a radiolabeled lubricant distributed in the subject’s colon. The tracer imaging was conducted via single photon emission computed tomography (SPECT), a non-invasive, in-vivo functional imaging technique. We develop a novel principal curve algorithm to construct a three dimensional curve through the colon images. The …


Ecologic Studies Revisited, Jon Wakefield May 2007

Ecologic Studies Revisited, Jon Wakefield

UW Biostatistics Working Paper Series

Ecologic studies use data aggregated over groups, rather than data on individuals. Such studies are popular since they may make use of existing data bases, and can offer large exposure variation if based on broad geographical areas. Unfortunately the aggregation of data that defines ecologic studies results in a loss of information that can lead to ecologic bias. Specifically, ecologic bias arises from the inability of ecologic data to characterize within-area variability in exposures and confounders. We describe in detail particular forms of ecologic bias so that their potential impact on any particular study may be assessed. The only way …


Gamma Generalized Linear Models For Pharmacokinetic Data, Ruth Salway, Jon Wakefield May 2007

Gamma Generalized Linear Models For Pharmacokinetic Data, Ruth Salway, Jon Wakefield

UW Biostatistics Working Paper Series

This paper considers the modeling of single dose pharmacoki- netic data. Traditionally, so-called compartmental models have been used to analyze such data. Unfortunately the mean function of such models are sums of exponentials for which inference and computation may not be straightfor- ward. We present an alternative to these models based on generalized linear models, for which desirable statistical properties exist, with a logarithmic link and gamma distribution. The latter has a constant coefficient of variation which is often appropriate for pharmacokinetic data. Inference is convenient from either a likelihood or a Bayesian perspective. We consider models for both single …


Biomarker Evaluation Using The Controls As A Reference Population, Ying Huang, Margaret Pepe Apr 2007

Biomarker Evaluation Using The Controls As A Reference Population, Ying Huang, Margaret Pepe

UW Biostatistics Working Paper Series

The classification accuracy of a continuous marker is typically evaluated with the Receiver Operating Characteristic Curve. In this paper, we study an alternative conceptual framework, the "percentile value". In particular the controls only provide a reference distribution to standardize the marker. The analysis proceeds by analyzing the standardized marker only in cases. The approach is shown to be equivalent to ROC analysis. Advantages are that it provides a framework more familiar to biostatisticians and it opens up avenues for new statistical techniques in biomarker evaluation. We develop several new procedures based on this framework for comparing biomarkers and for comparing …


A Bayesian Hierarchical Framework For Spatial Modeling Of Fmri Data, F. Dubois Bowman, Brian S. Caffo, Susan Spear Bassett, Clinton Kilts Apr 2007

A Bayesian Hierarchical Framework For Spatial Modeling Of Fmri Data, F. Dubois Bowman, Brian S. Caffo, Susan Spear Bassett, Clinton Kilts

Johns Hopkins University, Dept. of Biostatistics Working Papers

Functional neuroimaging techniques enable investigations into the neural basis of human cognition, emotions, and behaviors. In practice, applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric,neurological, and substance abuse disorders, as well as into the neural responses to their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. One may also extend voxel-level analyses by simultaneously considering the ensemble of voxels constituting an anatomically defined region of interest (ROI) or by considering means or quantiles of the ROI. In this work we present a Bayesian extension …


Fast Adaptive Penalized Splines, Tatyana Krivobokova, Ciprian M. Crainiceanu, Goran Kauermann Mar 2007

Fast Adaptive Penalized Splines, Tatyana Krivobokova, Ciprian M. Crainiceanu, Goran Kauermann

Johns Hopkins University, Dept. of Biostatistics Working Papers

This paper proposes a numerically simple routine for locally adaptive smoothing. The locally heterogeneous regression function is modelled as a penalized spline with a smoothly varying smoothing parameter modelled as another penalized spline. This is being formulated as hierarchical mixed model, with spline coe±cients following a normal distribution, which by itself has a smooth structure over the variances. The modelling exercise is in line with Baladandayuthapani, Mallick & Carroll (2005) or Crainiceanu, Ruppert & Carroll (2006). But in contrast to these papers Laplace's method is used for estimation based on the marginal likelihood. This is numerically simple and fast and …


A Flexible Semi-Parametric Approach To Estimating A Dose-Response Relationship: The Treatment Of Childhood Amblyopia. , David A. Stephens, Erica E M Moodie Mar 2007

A Flexible Semi-Parametric Approach To Estimating A Dose-Response Relationship: The Treatment Of Childhood Amblyopia. , David A. Stephens, Erica E M Moodie

COBRA Preprint Series

In a study of a dose-response relationship, flexibility in modelling is essential to capturing the treatment effect when the mean effect of other covariates is not fully understood, so that observed treatment effect is not due to the imposition of a rigid model for the relationship between response, treatment, and other variables. A semiparametric additive linear mixed (SPALM) model (Ruppert et al. 2003) provides a tractable and flexible approach to modelling the influence of potentially confounding variables. In this paper, we present pure likelihood and Bayesian versions of the SPALM model. Both methods of inference are readily implementable, but the …


Modified Test Statistics By Inter-Voxel Variance Shrinkage With An Application To Fmri, Shu-Chih Su, Brian Caffo, Elizabeth Garrett-Mayer, Susan Bassett Mar 2007

Modified Test Statistics By Inter-Voxel Variance Shrinkage With An Application To Fmri, Shu-Chih Su, Brian Caffo, Elizabeth Garrett-Mayer, Susan Bassett

Johns Hopkins University, Dept. of Biostatistics Working Papers

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of fMRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels in the statistical analysis of fMRI data is desirable in order …


Analysis Of Multi-Level Correlated Data In The Framework Of Generalized Estimating Equations Via Xtmultcorr Procedures In Stata And Qls Functions In Matlab, Justine Shults, Sarah J. Ratcliffe Jan 2007

Analysis Of Multi-Level Correlated Data In The Framework Of Generalized Estimating Equations Via Xtmultcorr Procedures In Stata And Qls Functions In Matlab, Justine Shults, Sarah J. Ratcliffe

UPenn Biostatistics Working Papers

No abstract provided.