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Articles 661 - 690 of 1108
Full-Text Articles in Statistics and Probability
Simultaneous Confidence Intervals Based On The Percentile Bootstrap Approach, Micha Mandel, Rebecca A. Betensky
Simultaneous Confidence Intervals Based On The Percentile Bootstrap Approach, Micha Mandel, Rebecca A. Betensky
Harvard University Biostatistics Working Paper Series
No abstract provided.
Random Effects Models In A Meta-Analysis Of The Accuracy Of Diagnostic Tests Within A Gold Standard In The Presence Of Missing Data, Haitao Chu, Sining Chen, Thomas A. Louis
Random Effects Models In A Meta-Analysis Of The Accuracy Of Diagnostic Tests Within A Gold Standard In The Presence Of Missing Data, Haitao Chu, Sining Chen, Thomas A. Louis
Johns Hopkins University, Dept. of Biostatistics Working Papers
In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian …
Identifying Patients Who Need Additional Biomarkers For Better Prediction Of Health Outcome Or Diagnosis Of Clinical Phenotype, Lu Tian, Tianxi Cai, L. J. Wei
Identifying Patients Who Need Additional Biomarkers For Better Prediction Of Health Outcome Or Diagnosis Of Clinical Phenotype, Lu Tian, Tianxi Cai, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Identifying Effect Modifiers In Air Pollution Time-Series Studies Using A Two-Stage Analysis, Sandrah P. Eckel, Thomas A. Louis
Identifying Effect Modifiers In Air Pollution Time-Series Studies Using A Two-Stage Analysis, Sandrah P. Eckel, Thomas A. Louis
Johns Hopkins University, Dept. of Biostatistics Working Papers
Studies of the health effects of air pollution such as the National Morbidity and Mortality Air Pollution Study (NMMAPS) relate changes in daily pollution to daily deaths in a sample of cities and calendar years. Generally, city-specific estimates are combined into regional and national estimates using two-stage models. Our two-stage analysis identifies effect modifiers of the relation between single-day lagged PM10 and daily mortality in people age 65 and older from the 50 largest NMMAPS cities. We build on the standard approach by "fractionating" city-specific analyses to produce month-year-city specific estimated air pollution effects (slopes) in Stage I. In Stage …
The Analysis Of Pixel Intensity (Myocardial Signal Density) Data: The Quantification Of Myocardial Perfusion By Imaging Methods., William F. Mccarthy, Douglas R. Thompson
The Analysis Of Pixel Intensity (Myocardial Signal Density) Data: The Quantification Of Myocardial Perfusion By Imaging Methods., William F. Mccarthy, Douglas R. Thompson
COBRA Preprint Series
This paper described a number of important issues in the analysis of pixel intensity data, as well as approaches for dealing with these. We particularly emphasized the issue of clustering, which may be ubiquitous in studies of pixel intensity data. Clustering can take many forms, e.g., measurements of different sections of a heart or repeated measurements of the same research participant. Clustering typically has the effect of increasing variance estimates. When one fails to account for clustering, variance estimates may be unrealistically small, resulting in spurious significance. We illustrated several possible approaches to account for clustering, including adjusting standard errors …
Coronary Evaluation Using Multi-Detector Spiral Computed Tomography Angiography: Statistical Design And Analysis, William F. Mccarthy, Douglas R. Thompson, Bruce A. Barton
Coronary Evaluation Using Multi-Detector Spiral Computed Tomography Angiography: Statistical Design And Analysis, William F. Mccarthy, Douglas R. Thompson, Bruce A. Barton
COBRA Preprint Series
Contrast-enhanced multi-detector row spiral computed tomography (MDCT) has been introduced as a method for non-invasive visualization of coronary artery stenosis. To determine the diagnostic accuracy of MDCT coronary angiography, as compared to the “gold standard” invasive coronary angiography, sensitivity and specificity are estimated (95% Confidence Intervals). Three separate levels of estimation are computed: at the patient level, at the coronary artery level, and at the coronary artery segment level. We review the methodology for the estimation of sensitivity and specificity of non-clustered binary data (patient level analysis) and present a methodology for the estimation of sensitivity and specificity that considers …
Bayesian Bivariate Image Analysis With Application To Dual Autoradiography, Timothy D. Johnson, Morand Piert
Bayesian Bivariate Image Analysis With Application To Dual Autoradiography, Timothy D. Johnson, Morand Piert
The University of Michigan Department of Biostatistics Working Paper Series
We present a Bayesian bivariate image model and apply it to a study that was designed to investigate the relationship between hypoxia and angiogenesis in an animal tumor model. Two radiolabeled tracers (one measuring angio- genesis, the other measuring hypoxia) were simultaneously injected into the animals, the tumors removed and autoradiographic images of the tracer concentrations were obtained. We model correlation between tracers with a mixture of bivariate normal distributions and the spatial correlation inherent in the images by means of the celebrated Potts model. Although the Potts model is typically used for image segmentation, we use it solely as …
Estimating The Effect Of Vigorous Physical Activity On Mortality In The Elderly Based On Realistic Individualized Treatment And Intention-To-Treat Rules, Oliver Bembom, Mark J. Van Der Laan
Estimating The Effect Of Vigorous Physical Activity On Mortality In The Elderly Based On Realistic Individualized Treatment And Intention-To-Treat Rules, Oliver Bembom, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks corresponding to hypothetical scenarios in which all subjects in the target population engage in a given level of vigorous physical activity. A causal effect defined on the basis of such a static treatment intervention can only be identified from observed data if all subjects in the target population have a positive probability of selecting each of the candidate treatment options, an assumption that is highly unrealistic in this case since …
Evaluating The Roc Performance Of Markers For Future Events, Margaret Pepe, Yingye Zheng, Yuying Jin
Evaluating The Roc Performance Of Markers For Future Events, Margaret Pepe, Yingye Zheng, Yuying Jin
UW Biostatistics Working Paper Series
Receiver operating characteristic (ROC) curves play a central role in the evaluation of biomarkers and tests for disease diagnosis. Predictors for event time outcomes can also be evaluated with ROC curves, but the time lag between marker measurement and event time must be acknowledged. We discuss different definitions of time-dependent ROC curves in the context of real applications. Several approaches have been proposed for estimation. We contrast retrospective versus prospective methods in regards to assumptions and flexibility, including their capacities to incorporate censored data, competing risks and different sampling schemes. Applications to two datasets are presented.
Analyzing Sequentially Randomized Trials Based On Causal Effect Models For Realistic Individualized Treatment Rules, Oliver Bembom, Mark J. Van Der Laan
Analyzing Sequentially Randomized Trials Based On Causal Effect Models For Realistic Individualized Treatment Rules, Oliver Bembom, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
In this paper, we argue that causal effect models for realistic individualized treatment rules represent an attractive tool for analyzing sequentially randomized trials. Unlike a number of methods proposed previously, this approach does not rely on the assumption that intermediate outcomes are discrete or that models for the distributions of these intermediate outcomes given the observed past are correctly specified. In addition, it generalizes the methodology for performing pairwise comparisons between individualized treatment rules by allowing the user to posit a marginal structural model for all candidate treatment rules simultaneously. If only a small number of candidate treatment rules are …
Quantitative Magnetic Resonance Image Analysis Via The Em Algorithm With Stochastic Variation, Xiaoxi Zhang, Timothy D. Johnson, Roderick J.A. Little
Quantitative Magnetic Resonance Image Analysis Via The Em Algorithm With Stochastic Variation, Xiaoxi Zhang, Timothy D. Johnson, Roderick J.A. Little
The University of Michigan Department of Biostatistics Working Paper Series
Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which, researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual’s response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying “true” scene via qMRI, due to measurement errors or unpredictable influences. We use …
Racial Disparities In Mortality Risks In A Sample Of The U.S. Medicare Population, Yijie Zhou, Francesca Dominici, Thomas A. Louis
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
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
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 …
Review Of The Maximum Likelihood Functions For Right Censored Data. A New Elementary Derivation., Stefano Patti, Elia Biganzoli, Patrizia Boracchi
Review Of The Maximum Likelihood Functions For Right Censored Data. A New Elementary Derivation., Stefano Patti, Elia Biganzoli, Patrizia Boracchi
COBRA Preprint Series
Censoring is a well known feature recurrent in the analysis of lifetime data, occurring in the model when exact lifetimes can be collected for only a representative portion of the surveyed individuals. If lifetimes are known only to exceed some given values, it is referred to as right censoring. In this paper we propose a systematization and a new derivation of the likelihood function for right censored sampling schemes; calculations are reported and assumptions are carefully stated. The sampling schemes considered (Type I, II and Random Censoring) give rise to the same ML function. Only the knowledge of elementary probability …
Ecologic Studies Revisited, Jon Wakefield
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
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 …
Evaluating A Group Sequential Design In The Setting Of Nonproportional Hazards, Daniel L. Gillen, Scott S. Emerson
Evaluating A Group Sequential Design In The Setting Of Nonproportional Hazards, Daniel L. Gillen, Scott S. Emerson
UW Biostatistics Working Paper Series
Group sequential methods have been widely described and implemented in a clinical trial setting where parametric and semiparametric models are deemed suitable. In these situations, the evaluation of the operating characteristics of a group sequential stopping rule remains relatively straightforward. However, in the presence of nonproportional hazards survival data nonparametric methods are often used, and the evaluation of stopping rules is no longer a trivial task. Specifically, nonparametric test statistics do not necessarily correspond to a parameter of clinical interest, thus making it difficult to characterize alternatives at which operating characteristics are to be computed. We describe an approach for …
Biomarker Evaluation Using The Controls As A Reference Population, Ying Huang, Margaret Pepe
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 …
Bayesian Spatial Modeling Of Fmri Data: A Multiple-Subject Analysis, Lei Xu, Timothy Johnson, Thomas Nichols
Bayesian Spatial Modeling Of Fmri Data: A Multiple-Subject Analysis, Lei Xu, Timothy Johnson, Thomas Nichols
The University of Michigan Department of Biostatistics Working Paper Series
The aim of this work is to develop a spatial model for multi-subject fMRI data. While there has been much work on univariate modeling of each voxel for single- and multi-subject data, and some work on spatial modeling for single-subject data, there has been no work on spatial models that explicitly account for intersubject variability in activation location. We use a Bayesian hierarchical spatial model to fit the data. At the first level we model "population centers" that mark the centers of regions of activation. For a given population center each subject may have zero or more associated "individual components". …
A Bayesian Hierarchical Framework For Spatial Modeling Of Fmri Data, F. Dubois Bowman, Brian S. Caffo, Susan Spear Bassett, Clinton Kilts
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 …
Covariate Adjustment In Randomized Trials With Binary Outcomes: Targeted Maximum Likelihood Estimation, Kelly L. Moore, Mark J. Van Der Laan
Covariate Adjustment In Randomized Trials With Binary Outcomes: Targeted Maximum Likelihood Estimation, Kelly L. Moore, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Covariate adjustment using linear models for continuous outcomes in randomized trials has been shown to increase efficiency and power over the unadjusted method in estimating the marginal effect of treatment. However, for binary outcomes, investigators generally rely on the unadjusted estimate as the literature indicates that covariate-adjusted estimates based on logistic regression models are less efficient. The crucial step that has been missing when adjusting for covariates is that one must integrate/average the adjusted estimate over those covariates in order to obtain the marginal effect. We apply the method of targeted maximum likelihood estimation (MLE), as presented in van der …
What Is The Best Reference Rna? And Other Questions Regarding The Design And Analysis Of Two-Color Microarray Experiments, Kathleen F. Kerr, Kyle A. Serikawa, Caimiao Wei, Mette A. Peters, Roger E. Bumgarner
What Is The Best Reference Rna? And Other Questions Regarding The Design And Analysis Of Two-Color Microarray Experiments, Kathleen F. Kerr, Kyle A. Serikawa, Caimiao Wei, Mette A. Peters, Roger E. Bumgarner
UW Biostatistics Working Paper Series
The reference design is a practical and popular choice for microarray studies using two-color platforms. In the reference design, the reference RNA uses half of all array resources, leading investigators to ask: What is the best reference RNA? We propose a novel method for evaluating reference RNAs and present the results of an experiment that was specially designed to evaluate three common choices of reference RNA. We found no compelling evidence in favor of any particular reference. In particular, a commercial reference showed no advantage in our data. Our experimental design also enabled a new way to test the effectiveness …
Fast Adaptive Penalized Splines, Tatyana Krivobokova, Ciprian M. Crainiceanu, Goran Kauermann
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
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 …
A Bayesian Hierarchical Model For Spot Fluorescence In Microarrays, Federico Mattia Stefanini
A Bayesian Hierarchical Model For Spot Fluorescence In Microarrays, Federico Mattia Stefanini
COBRA Preprint Series
Microarray experiments are characterized by the presence of many sources of experimental bias and a remarkably large technical variability. The assessment of differential expression for genes transcribed into a small number of mRNA copies heavily depends on the proper quantification of background fluorescence within spot. The rough model `observed = hybridization plus background' fluorescence is at first reformulated at spot level, then it is embedded into a Bayesian hierarchical model suited for fitting control spots. The novelties of the approach include the background correction performed on the latent mean of replicated spots, and an explicit model for outlying observations at …
False Discovery Rate Analysis Of Brain Diffusion Direction Maps, Armin Schwartzman, Robert F. Dougherty, Jonathan E. Taylor
False Discovery Rate Analysis Of Brain Diffusion Direction Maps, Armin Schwartzman, Robert F. Dougherty, Jonathan E. Taylor
COBRA Preprint Series
Diffusion tensor imaging (DTI) is a novel modality of magnetic resonance imaging that allows non-invasive mapping of the brain’s white matter. A particular map derived from DTI measurements is a map of water principal diffusion directions, which are proxies for neural fiber directions. We consider an experiment in which diffusion direction maps were acquired for two groups of subjects. The objective of the analysis is to find regions of the brain in which the corresponding diffusion directions differ between the groups. This is attained by first computing a test statistic for the difference in direction at every brain location using …
On Comparing The Clustering Of Regression Models Method With K-Means Clustering, Li-Xuan Qin, Steven G. Self
On Comparing The Clustering Of Regression Models Method With K-Means Clustering, Li-Xuan Qin, Steven G. Self
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
Gene clustering is a common question addressed with microarray data. Previous methods, such as K-means clustering and hierarchical clustering, base gene clustering directly on the observed measurements. A new model-based clustering method, the clustering of regression models (CORM) method, bases the clustering of genes on their relationship to covariates. It explicitly models different sources of variations and bases gene clustering solely on the systematic variation. Both being partitional clustering, CORM is closely related to K-means clustering. In this paper, we discuss the relationship between the two clustering methods in terms of both model formulation and implications on other important aspects …
Conservative Estimation Of Optimal Multiple Testing Procedures, James E. Signorovitch
Conservative Estimation Of Optimal Multiple Testing Procedures, James E. Signorovitch
Harvard University Biostatistics Working Paper Series
No abstract provided.
Modified Test Statistics By Inter-Voxel Variance Shrinkage With An Application To Fmri, Shu-Chih Su, Brian Caffo, Elizabeth Garrett-Mayer, Susan Bassett
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 …