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Articles 481 - 510 of 567
Full-Text Articles in Biostatistics
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 …
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 …
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 …
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 …
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 …
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 …
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
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.
Lehmann Family Of Roc Curves, Mithat Gonen, Glenn Heller
Lehmann Family Of Roc Curves, Mithat Gonen, Glenn Heller
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
Receiver operating characteristic (ROC) curves are useful in evaluating the ability of a continuous marker in discriminating between the two states of a binary outcome such as diseased/not diseased. The most popular parametric model for an ROC curve is the binormal model which assumes that the marker is normally distributed conditional on the outcome. Here we present an alternative to the binormal model based on the Lehmann family, also known as the proportional hazards specification. The resulting ROC curve and its functionals (such as the area under the curve) have simple analytic forms. We derive closed-form expressions for the asymptotic …
A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano
A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Semiparametric Approach For The Nonparametric Transformation Survival Model With Multiple Covariates, Xiao Song, Shuangge Ma, Jian Huang, Xiao-Hua Zhou
A Semiparametric Approach For The Nonparametric Transformation Survival Model With Multiple Covariates, Xiao Song, Shuangge Ma, Jian Huang, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
The nonparametric transformation model for survival time that makes no parametric assumptions on both the transformation function and the error is appealing in its flexibility. The nonparametric transformation model makes no assumption on the forms of the transformation function and the error distribution. This model is appealing in its flexibility for modeling censored survival data. Current approaches for estimation of the regression parameters involve maximizing discontinuous objective functions, which are numerically infeasible to implement in the case of multiple covariates. Based on the partial rank estimator (Khan & Tamer, 2004), we propose a smoothed partial rank estimator which maximizes a …
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
The University of Michigan Department of Biostatistics Working Paper Series
SUMMARY. We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model …
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Analysis Of Case-Control Age-At-Onset Data Using A Modified Case-Cohort Method, Bin Nan, Xihong Lin
Analysis Of Case-Control Age-At-Onset Data Using A Modified Case-Cohort Method, Bin Nan, Xihong Lin
The University of Michigan Department of Biostatistics Working Paper Series
Case-control designs are widely used in rare disease studies. In a typical case-control study, data are collected from a sample of all available subjects who have experienced a disease (cases) and a sub-sample of subjects who have not experienced the disease (controls) in a study cohort. Cases are often oversampled in case-control studies. Logistic regression is a common tool to estimate the relative risks of the disease and a set of covariates. Very often in such a study, information of ages-at-onset of the disease for all cases and ages at survey of controls are known. Standard logistic regression analysis using …
Doubly Penalized Buckley-James Method For Survival Data With High-Dimensional Covariates, Sijian Wang, Bin Nan, Ji Zhu, David G. Beer
Doubly Penalized Buckley-James Method For Survival Data With High-Dimensional Covariates, Sijian Wang, Bin Nan, Ji Zhu, David G. Beer
The University of Michigan Department of Biostatistics Working Paper Series
Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley-James method for the semiparametric accelerated failure time model to relate high-dimensional genomic data to censored survival outcomes, which uses a mixture of L1-norm and L2-norm penalties. Similar to the elastic-net method for linear regression model with uncensored data, the proposed method performs automatic gene selection and parameter estimation, where highly correlated genes are able to be selected (or removed) together. The two-dimensional tuning parameter is determined by cross-validation and uniform design. …
Exploiting Gene-Environment Independence For Analysis Of Case-Control Studies: An Empirical Bayes Approach To Trade Off Between Bias And Efficiency, Bhramar Mukherjee, Nilanjan Chatterjee
Exploiting Gene-Environment Independence For Analysis Of Case-Control Studies: An Empirical Bayes Approach To Trade Off Between Bias And Efficiency, Bhramar Mukherjee, Nilanjan Chatterjee
The University of Michigan Department of Biostatistics Working Paper Series
Standard prospective logistic regression analysis of case-control data often leads to very imprecise estimates of gene-environment interactions due to small numbers of cases or controls in cells of crossing genotype and exposure. In contrast, under the assumption of gene-environment independence, modern “retrospective” methods, including the “case-only” approach, can estimate the interaction parameters much more precisely, but they can be seriously biased when the underlying assumption of gene-environment independence is violated. In this article, we propose a novel approach to analyze case-control data that can relax the gene-environment independence assumption using an empirical Bayes framework. In the special case, involving a …
A Note On Bias Due To Fitting Prospective Multivariate Generalized Linear Models To Categorical Outcomes Ignoring Retrospective Sampling Schemes, Bhramar Mukherjee, Ivy Liu
A Note On Bias Due To Fitting Prospective Multivariate Generalized Linear Models To Categorical Outcomes Ignoring Retrospective Sampling Schemes, Bhramar Mukherjee, Ivy Liu
The University of Michigan Department of Biostatistics Working Paper Series
Outcome dependent sampling designs are commonly used in economics, market research and epidemiological studies. Case-control sampling design is a classic example of outcome dependent sampling, where exposure information is collected on subjects conditional on their disease status. In many situations, the outcome under consideration may have multiple categories instead of a simple dichotomization. For example, in a case-control study, there may be disease sub-classification among the “cases” based on progression of the disease, or in terms of other histological and morphological characteristics of the disease. In this note, we investigate the issue of fitting prospective multivariate generalized linear models to …
Covariate Specific Roc Curve With Survival Outcome, Xiao Song, Xiao-Hua Zhou
Covariate Specific Roc Curve With Survival Outcome, Xiao Song, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
The receiver operating characteristic (ROC) curve has been extended to survival data recently, including the nonparametric approach by Heagerty, Lumley and Pepe (2000) and the semiparametric approach by Heagerty and Zheng (2005) using standard survival analysis techniques based on two different time-dependent ROC curve definitions. However, both approaches cannot adjust for the effect of covariates on the accuracy of the biomarker. To account for the covariate effect, we propose semiparametric models for covariate specific ROC curves corresponding to the two time-dependent ROC curve definitions, respectively. We show that the estimators are consistent and converge to Gaussian processes. In the case …
Conditional Likelihood Methods For Haplotype-Based Association Analysis Using Matched Case-Control Data, Jinbo Chen, Carmen Rodriguez
Conditional Likelihood Methods For Haplotype-Based Association Analysis Using Matched Case-Control Data, Jinbo Chen, Carmen Rodriguez
UPenn Biostatistics Working Papers
Genetic epidemiologists routinely assess disease susceptibility in relation to haplotypes, i.e., combinations of alleles on a single chromosome. We study statistical methods for inferring haplotype-related disease risk using SNP genotype data from matched case-control studies, where controls are individually matched to cases on some selected factors. Assuming a logistic regression model for haplotype-disease association, we propose two conditional likelihood approaches that address the issue that haplotypes cannot be inferred with certainty from SNP genotype data (phase ambiquity). One approach is based on the likelihood of disease status conditioned on the total number of cases, genotypes, and other covariates within each …
Generalized Confidence Intervals For The Ratio Or Difference Of Two Means For Lognormal Populations With Zeros, Yea-Hung Chen, Xiao-Hua Zhou
Generalized Confidence Intervals For The Ratio Or Difference Of Two Means For Lognormal Populations With Zeros, Yea-Hung Chen, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
We discuss in this article methods for analyzing lognormal data that may include zeros. Specifically, we are interested in interval estimation for the ratio or difference of the population means. We propose here two generalized pivotal (GP) approaches: a ``true'' GP method and an ``approximate'' GP method. Additionally, we propose two likelihood-based approaches: a signed log-likelihood ratio (SLLR) method and a modified SLLR method. Our simulation studies suggest that the approximate generalized pivotal approach outperforms all other known methods; it results in highly accurate coverage frequencies and fairly low bias, even in small sample settings.
Multiple Imputation - Review Of Theory, Implementation And Software, Ofer Harel, Xiao-Hua Zhou
Multiple Imputation - Review Of Theory, Implementation And Software, Ofer Harel, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
Missing data is a common complication in data analysis. In many medical settings missing data can cause difficulties in estimation, precision and inference. Multiple imputation (MI) \cite{Rubin87} is a simulation based approach to deal with incomplete data. Although there are many different methods to deal with incomplete data, MI has become one of the leading methods. Since the late 80's we observed a constant increase in the use and publication of MI related research. This tutorial does not attempt to cover all the material concerning MI, but rather provides an overview and combines together the theory behind MI, the implementation …
Multiple Imputation For The Comparison Of Two Screening Tests In Two-Phase Alzheimer Studies, Ofer Harel, Xiao-Hua Zhou
Multiple Imputation For The Comparison Of Two Screening Tests In Two-Phase Alzheimer Studies, Ofer Harel, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
Two-phase designs are common in epidemiological studies of dementia, and especially in Alzheimer research. In the first phase, all subjects are screened using a common screening test(s), while in the second phase, only a subset of these subjects is tested using a more definitive verification assessment, i.e. golden standard test. When comparing the accuracy of two screening tests in a two-phase study of dementia, inferences are commonly made using only the verified sample. It is well documented that in that case, there is a risk for bias, called verification bias. When the two screening tests have only two values (e.g. …
Improved Generalized Estimating Equation Analysis Via Xtqls For Implementation Of Quasi-Least Squares In Stata, Justine Shults, Sarah J. Ratcliffe, Mary Leonard
Improved Generalized Estimating Equation Analysis Via Xtqls For Implementation Of Quasi-Least Squares In Stata, Justine Shults, Sarah J. Ratcliffe, Mary Leonard
UPenn Biostatistics Working Papers
No abstract provided.
Generalized Monotonic Functional Mixed Models With Application To Modeling Normal Tissue Complications , Matthew Schipper, Jeremy Taylor, Xihong Lin
Generalized Monotonic Functional Mixed Models With Application To Modeling Normal Tissue Complications , Matthew Schipper, Jeremy Taylor, Xihong Lin
The University of Michigan Department of Biostatistics Working Paper Series
Normal tissue complications are a common side effect of radiation therapy. They are the consequence of the dose of radiation received by the normal tissue surrounding the tumor site. It is not known what function of the dose distribution to the normal tissue drives the presence and severity of the complications. Regarding the density of the dose distribution as a curve, a summary measure is obtained by integrating a weighting function of dose (w(d)) over the dose density. For biological reasons the weight function should be monotonic. We propose to study the dose effect on a clinical outcome using a …
Predicting Future Responses Based On Possibly Misspecified Working Models, Tianxi Cai, Lu Tian, Scott D. Solomon, L.J. Wei
Predicting Future Responses Based On Possibly Misspecified Working Models, Tianxi Cai, Lu Tian, Scott D. Solomon, L.J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Permutation Methods In Relative Risk Regression Models, Wenyu Jiang, Jack Kalbfleisch
Permutation Methods In Relative Risk Regression Models, Wenyu Jiang, Jack Kalbfleisch
The University of Michigan Department of Biostatistics Working Paper Series
In this paper, we develop a weighted permutation (WP) method to construct confidence intervals for regression parameters in relative risk regression models. The WP method is a generalized permutation approach. It constructs a resampled history which mimics the observed history for individuals under study. Inference procedures are based on studentized score statistics that are insensitive to the forms of the relative risk function. This makes the WP method appealing in the general framework of the relative risk regression model. First order accuracy of the WP method is established using the counting process approach with a partial likelihood filtration. A simulation …
On The Potential For Ill-Logic With Logically Defined Outcomes, Xianbin Li, Brian S. Caffo, Daniel O. Scharfstein
On The Potential For Ill-Logic With Logically Defined Outcomes, Xianbin Li, Brian S. Caffo, Daniel O. Scharfstein
Johns Hopkins University, Dept. of Biostatistics Working Papers
Logically defined outcomes are commonly used in medical diagnoses and epidemiological research. When missing values in the original outcomes exist, the method of handling the missingness can have unintended consequences, even if the original outcomes are missing completely at random. Complicating the issue is that the default behavior of standard statistical packages yields different results. In this paper, we consider two binary original outcomes, which are missing completely at random. For estimating the prevalence of a logically defined "or" outcome, we discuss the properties of four estimators: complete case estimator, all-available case estimator, maximum likelihood estimator (MLE), and moment-based estimator. …
Evaluating Causal Effect Predictiveness Of Candidate Surrogate Endpoints, Peter B. Gilbert, Michael Hudgens
Evaluating Causal Effect Predictiveness Of Candidate Surrogate Endpoints, Peter B. Gilbert, Michael Hudgens
UW Biostatistics Working Paper Series
Most methods for evaluating surrogate endpoints measure validity in terms of net effects (i.e., treatment effects adjusted for the biomarker measured after randomization). Frangakis and Rubin (2002, Biometrics) criticized these approaches because net effects may reflect selection bias, and suggested an alternative definition of a surrogate endpoint (a "principal" surrogate) based on causal effects. For evaluating principal surrogates we introduce a causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. The CEP surface is not identifiable in general due to missing potential outcomes. However, by incorporating …