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911 records returned

Augmented Cross-Sectional Prevalence Testing for Estimating HIV IncidenceEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Estimation of an HIV incidence rate based on a cross-sectional sample of individuals evaluated with both a sensitive and less-sensitive diagnostic test offers important advantages to incidence estimation based on a longitudinal cohort study. However, the reliability of the cross-sectional approach has been called into question because of two major concerns. One is the difficulty in obtaining a reliable external approximation for the mean "window period" between detectability of HIV infection with the sensitive and less-sensitive test, which is used in the cross-sectional estimation procedure. The other is how to handle fal...
Source: Biometrics - November 14, 2009 Category: Biotechnology Authors: Rui Wang, Stephen W. Lagakos Source Type: journals

Markov and Semi-Markov Switching Linear Mixed Models Used to Identify Forest Tree Growth ComponentsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Tree growth is assumed to be mainly the result of three components: (i) an endogenous component assumed to be structured as a succession of roughly stationary phases separated by marked change points that are asynchronous among individuals, (ii) a time-varying environmental component assumed to take the form of synchronous fluctuations among individuals, and (iii) an individual component corresponding mainly to the local environment of each tree. To identify and characterize these three components, we propose to use semi-Markov switching linear mixed models, i.e., models that combine linear mixed models in a semi-Markovian...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Florence Chaubert-Pereira, Yann Guédon, Christian Lavergne, Catherine Trottier Source Type: journals

Semi-Markov Models with Phase-Type Sojourn DistributionsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Continuous-time multistate models are widely used for categorical response data, particularly in the modeling of chronic diseases. However, inference is difficult when the process is only observed at discrete time points, with no information about the times or types of events between observation times, unless a Markov assumption is made. This assumption can be limiting as rates of transition between disease states might instead depend on the time since entry into the current state. Such a formulation results in a semi-Markov model. We show that the computational problems associated with fitting semi-Markov models to panel-...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Andrew C. Titman, Linda D. Sharples Source Type: journals

Statistical Analysis of Illness–Death Processes and Semicompeting Risks DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In many instances, a subject can experience both a nonterminal and terminal event where the terminal event (e.g., death) censors the nonterminal event (e.g., relapse) but not vice versa. Typically, the two events are correlated. This situation has been termed semicompeting risks (e.g., Fine, Jiang, and Chappell, 2001, Biometrika 88, 907[ndash]939; Wang, 2003, Journal of the Royal Statistical Society, Series B 65, 257[ndash]273), and analysis has been based on a joint survival function of two event times over the positive quadrant but with observation restricted to the upper wedge. Implicitly, this approach entertains the i...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Jinfeng Xu, John D. Kalbfleisch, Beechoo Tai Source Type: journals

Pairwise Variable Selection for High-Dimensional Model-Based ClusteringEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Variable selection for clustering is an important and challenging problem in high-dimensional data analysis. Existing variable selection methods for model-based clustering select informative variables in a "one-in-all-out" manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applications, however, it is of interest to further establish exactly which clusters are separable by each informative variable. To address this question, we propose a pairwise variable selection method for high-dimensional model-based clust...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Jian Guo, Elizaveta Levina, George Michailidis, Ji Zhu Source Type: journals

Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential EquationsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Growth curve data consist of repeated measurements of a continuous growth process over time in a population of individuals. These data are classically analyzed by nonlinear mixed models. However, the standard growth functions used in this context prescribe monotone increasing growth and can fail to model unexpected changes in growth rates. We propose to model these variations using stochastic differential equations (SDEs) that are deduced from the standard deterministic growth function by adding random variations to the growth dynamics. A Bayesian inference of the parameters of these SDE mixed models is developed. In the c...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Sophie Donnet, Jean-Louis Foulley, Adeline Samson Source Type: journals

Association Tests for a Censored Quantitative Trait and Candidate Genes in Structured Populations with Multilevel Genetic RelatednessEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Several statistical methods for detecting associations between quantitative traits and candidate genes in structured populations have been developed for fully observed phenotypes. However, many experiments are concerned with failure-time phenotypes, which are usually subject to censoring. In this article, we propose statistical methods for detecting associations between a censored quantitative trait and candidate genes in structured populations with complex multiple levels of genetic relatedness among sampled individuals. The proposed methods correct for continuous population stratification using both population structure ...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Meijuan Li, Cavan Reilly, Tim Hanson Source Type: journals

Model-Based Quality Assessment and Base-Calling for Second-Generation Sequencing DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Second-generation sequencing (sec-gen) technology can sequence millions of short fragments of DNA in parallel, making it capable of assembling complex genomes for a small fraction of the price and time of previous technologies. In fact, a recently formed international consortium, the 1000 Genomes Project, plans to fully sequence the genomes of approximately 1200 people. The prospect of comparative analysis at the sequence level of a large number of samples across multiple populations may be achieved within the next five years. These data present unprecedented challenges in statistical analysis. For instance, analysis opera...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Héctor Corrada Bravo, Rafael A. Irizarry Source Type: journals

Bootstrap and Second-Order Tests of Risk DifferenceEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Clinical trials data often come in the form of low-dimensional tables of small counts. Standard approximate tests such as score and likelihood ratio tests are imperfect in several respects. First, they can give quite different answers from the same data. Second, the actual type-1 error can differ significantly from nominal, even for quite large sample sizes. Third, exact inferences based on these can be strongly nonmonotonic functions of the null parameter and lead to confidence sets that are discontiguous. There are two modern approaches to small sample inference. One is to use so-called higher order asymptotics (Reid, 20...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Chris J. Lloyd Source Type: journals

Modeling the Spatial and Temporal Dependence in fMRI DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Functional magnetic resonance imaging (fMRI) data sets are large and characterized by complex dependence structures driven by highly sophisticated neurophysiology and aspects of the experimental designs. Typical analyses investigating task-related changes in measured brain activity use a two-stage procedure in which the first stage involves subject-specific models and the second-stage specifies group (or population) level parameters. Customarily, the first-level accounts for temporal correlations between the serial scans acquired during one scanning session. Despite accounting for these correlations, fMRI studies often inc...
Source: Biometrics - November 13, 2009 Category: Biotechnology Authors: Gordana Derado, F. DuBois Bowman, Clinton D. Kilts Source Type: journals

The authors replied as follows:Email this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
(Source: Biometrics)
Source: Biometrics - November 10, 2009 Category: Biotechnology Authors: Wen Ye, Jeremy M.G. Taylor, Xihong Lin Source Type: journals

Detecting Genomic Aberrations Using Products in a Multiscale AnalysisEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Genomic instability, such as copy-number losses and gains, occurs in many genetic diseases. Recent technology developments enable researchers to measure copy numbers at tens of thousands of markers simultaneously. In this article, we propose a nonparametric approach for detecting the locations of copy-number changes and provide a measure of significance for each change point. The proposed test is based on seeking scale-based changes in the sequence of copy numbers, which is ordered by the marker locations along the chromosome. The method leads to a natural way to estimate the null distribution for the test of a change poin...
Source: Biometrics - October 10, 2009 Category: Biotechnology Authors: Xuesong Yu, Timothy W. Randolph, Hua Tang, Li Hsu Source Type: journals

Bayesian Variable Selection for Multivariate Spatially Varying Coefficient RegressionEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Physical activity has many well-documented health benefits for cardiovascular fitness and weight control. For pregnant women, the American College of Obstetricians and Gynecologists currently recommends 30 minutes of moderate exercise on most, if not all, days; however, very few pregnant women achieve this level of activity. Traditionally, studies have focused on examining individual or interpersonal factors to identify predictors of physical activity. There is a renewed interest in whether characteristics of the physical environment in which we live and work may also influence physical activity levels. We consider one of ...
Source: Biometrics - October 9, 2009 Category: Biotechnology Authors: Brian J. Reich, Montserrat Fuentes, Amy H. Herring, Kelly R. Evenson Source Type: journals

Estimating Treatment Effects of Longitudinal Designs using Regression Models on Propensity ScoresEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We derive regression estimators that can compare longitudinal treatments using only the longitudinal propensity scores as regressors. These estimators, which assume knowledge of the variables used in the treatment assignment, are important for reducing the large dimension of covariates for two reasons. First, if the regression models on the longitudinal propensity scores are correct, then our estimators share advantages of correctly specified model-based estimators, a benefit not shared by estimators based on weights alone. Second, if the models are incorrect, the misspecification can be more easily limited through model c...
Source: Biometrics - October 9, 2009 Category: Biotechnology Authors: Aristide C. Achy-Brou, Constantine E. Frangakis, Michael Griswold Source Type: journals

A Generalized Concordance Correlation Coefficient Based on the Variance Components Generalized Linear Mixed Models for Overdispersed Count DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The classical concordance correlation coefficient (CCC) to measure agreement among a set of observers assumes data to be distributed as normal and a linear relationship between the mean and the subject and observer effects. Here, the CCC is generalized to afford any distribution from the exponential family by means of the generalized linear mixed models (GLMMs) theory and applied to the case of overdispersed count data. An example of CD34+ cell count data is provided to show the applicability of the procedure. In the latter case, different CCCs are defined and applied to the data by changing the GLMM that fits the data. A ...
Source: Biometrics - October 9, 2009 Category: Biotechnology Authors: Josep L. Carrasco Source Type: journals

Utilizing Gaussian Markov Random Field Properties of Bayesian Animal ModelsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In this article, we demonstrate how Gaussian Markov random field properties give large computational benefits and new opportunities for the Bayesian animal model. We make inference by computing the posteriors for important quantitative genetic variables. For the single-trait animal model, a nonsampling-based approximation is presented. For the multitrait model, we set up a robust and fast Markov chain Monte Carlo algorithm. The proposed methodology was used to analyze quantitative genetic properties of morphological traits of a wild house sparrow population. Results for single- and multitrait models were compared. (Source: Biometrics)
Source: Biometrics - October 9, 2009 Category: Biotechnology Authors: Ingelin Steinsland, Henrik Jensen Source Type: journals

On Efficiency of Constrained Longitudinal Data Analysis Versus Longitudinal Analysis of CovarianceEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In randomized clinical trials, measurements are often collected on each subject at a baseline visit and several post-randomization time points. The longitudinal analysis of covariance in which the postbaseline values form the response vector and the baseline value is treated as a covariate can be used to evaluate the treatment differences at the postbaseline time points. Liang and Zeger (2000, Sankhyā: The Indian Journal of Statistics, Series B 62, 134[ndash]148) propose a constrained longitudinal data analysis in which the baseline value is included in the response vector together with the postbaseline values and a...
Source: Biometrics - September 17, 2009 Category: Biotechnology Authors: Kaifeng Lu Source Type: journals

Segmentation and Estimation for SNP Microarrays: A Bayesian Multiple Change-Point ApproachEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
High-density single-nucleotide polymorphism (SNP) microarrays provide a useful tool for the detection of copy number variants (CNVs). The analysis of such large amounts of data is complicated, especially with regard to determining where copy numbers change and their corresponding values. In this article, we propose a Bayesian multiple change-point model (BMCP) for segmentation and estimation of SNP microarray data. Segmentation concerns separating a chromosome into regions of equal copy number differences between the sample of interest and some reference, and involves the detection of locations of copy number difference ch...
Source: Biometrics - September 16, 2009 Category: Biotechnology Authors: Yu Chuan Tai, Mark N. Kvale, John S. Witte Source Type: journals

Estimating Haplotype Effects for Survival DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including...
Source: Biometrics - September 16, 2009 Category: Biotechnology Authors: Thomas H. Scheike, Torben Martinussen, Jeremy D. Silver Source Type: journals

Identifiability of Models for Multiple Diagnostic Testing in the Absence of a Gold StandardEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We present illustrations using simulated and real data. (Source: Biometrics)
Source: Biometrics - September 16, 2009 Category: Biotechnology Authors: Geoffrey Jones, Wesley O. Johnson, Timothy E. Hanson, Ronald Christensen Source Type: journals

Modeling Complex Phenotypes: Generalized Linear Models Using Spectrogram Predictors of Animal Communication SignalsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
A major goal of evolutionary biology is to understand the dynamics of natural selection within populations. The strength and direction of selection can be described by regressing relative fitness measurements on organismal traits of ecological significance. However, many important evolutionary characteristics of organisms are complex, and have correspondingly complex relationships to fitness. Secondary sexual characteristics such as mating displays are prime examples of complex traits with important consequences for reproductive success. Typically, researchers atomize sexual traits such as mating signals into a set of meas...
Source: Biometrics - September 16, 2009 Category: Biotechnology Authors: Scott H. Holan, Christopher K. Wikle, Laura E. Sullivan-Beckers, Reginald B. Cocroft Source Type: journals

A Score Test for Association of a Longitudinal Marker and an Event with Missing DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Often clinical studies periodically record information on disease progression as well as results from laboratory studies that are believed to reflect the progressing stages of the disease. A primary aim of such a study is to determine the relationship between the lab measurements and a disease progression. If there were no missing or censored data, these analyses would be straightforward. However, often patients miss visits, and return after their disease has progressed. In this case, not only is their progression time interval censored, but their lab test series is also incomplete. In this article, we propose a simple tes...
Source: Biometrics - September 15, 2009 Category: Biotechnology Authors: Dianne M. Finkelstein, Rui Wang, Linda H. Ficociello, David A. Schoenfeld Source Type: journals

Dynamic Model for Multivariate Markers of FecundabilityEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Dynamic latent class models provide a flexible framework for studying biologic processes that evolve over time. Motivated by studies of markers of the fertile days of the menstrual cycle, we propose a discrete-time dynamic latent class framework, allowing change points to depend on time, fixed predictors, and random effects. Observed data consist of multivariate categorical indicators, which change dynamically in a flexible manner according to latent class status. Given the flexibility of the framework, which incorporates semi-parametric components using mixtures of betas, identifiability constraints are needed to define t...
Source: Biometrics - September 14, 2009 Category: Biotechnology Authors: Bo Cai, David B. Dunson, Joseph B. Stanford Source Type: journals

A Semiparametric Missing-Data-Induced Intensity Method for Missing Covariate Data in Individually Matched Case–Control StudiesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In individually matched case[ndash]control studies, when some covariates are incomplete, an analysis based on the complete data may result in a large loss of information both in the missing and completely observed variables. This usually results in a bias and loss of efficiency. In this article, we propose a new method for handling the problem of missing covariate data based on a missing-data-induced intensity approach when the missingness mechanism does not depend on case[ndash]control status and show that this leads to a generalization of the missing indicator method. We derive the asymptotic properties of the estimates ...
Source: Biometrics - September 13, 2009 Category: Biotechnology Authors: Mulugeta Gebregziabher, Bryan Langholz Source Type: journals

Spatial Cluster Detection for Weighted Outcomes Using Cumulative Geographic ResidualsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
This article proposes a new class of spatial cluster detection methods for point or aggregate data, comprising of continuous, binary, and count data. Compared with the existing spatial cluster detection methods it has the following advantages. First, it readily incorporates region-specific weights, for example, based on a region's population or a region's outcome variance, which is the key for aggregate data. Second, the established general framework allows for area-level and individual-level covariate adjustment. A simulation study is conducted to evaluate the performance of the method. The proposed method is then applied...
Source: Biometrics - September 13, 2009 Category: Biotechnology Authors: Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari Source Type: journals

On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage ProcedureEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Ye, Lin, and Taylor (2008, Biometrics 64, 1238[ndash]1246) proposed a joint model for longitudinal measurements and time-to-event data in which the longitudinal measurements are modeled with a semiparametric mixed model to allow for the complex patterns in longitudinal biomarker data. They proposed a two-stage regression calibration approach that is simpler to implement than a joint modeling approach. In the first stage of their approach, the mixed model is fit without regard to the time-to-event data. In the second stage, the posterior expectation of an individual's random effects from the mixed-model are included as cova...
Source: Biometrics - September 13, 2009 Category: Biotechnology Authors: Paul S. Albert, Joanna H. Shih Source Type: journals

Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement ErrorEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the rel...
Source: Biometrics - August 10, 2009 Category: Biotechnology Authors: Samiran Sinha, Bani K. Mallick, Victor Kipnis, Raymond J. Carroll Source Type: journals

On Distance-Based Permutation Tests for Between-Group ComparisonsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Permutation tests based on distances among multivariate observations have found many applications in the biological sciences. Two major testing frameworks of this kind are multiresponse permutation procedures and pseudo-F tests arising from a distance-based extension of multivariate analysis of variance. In this article, we derive conditions under which these two frameworks are equivalent. The methods and equivalence results are illustrated by reanalyzing an ecological data set and by a novel application to functional magnetic resonance imaging data. (Source: Biometrics)
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: Philip T. Reiss, M. Henry H. Stevens, Zarrar Shehzad, Eva Petkova, Michael P. Milham Source Type: journals

Semiparametric Bayes Multiple Testing: Applications to Tumor DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In National Toxicology Program (NTP) studies, investigators want to assess whether a test agent is carcinogenic overall and specific to certain tumor types, while estimating the dose-response profiles. Because there are potentially correlations among the tumors, a joint inference is preferred to separate univariate analyses for each tumor type. In this regard, we propose a random effect logistic model with a matrix of coefficients representing log-odds ratios for the adjacent dose groups for tumors at different sites. We propose appropriate nonparametric priors for these coefficients to characterize the correlations and to...
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: Lianming Wang, David B. Dunson Source Type: journals

Utility-Based Optimization of Combination Therapy Using Ordinal Toxicity and Efficacy in Phase I/II TrialsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
An outcome-adaptive Bayesian design is proposed for choosing the optimal dose pair of a chemotherapeutic agent and a biological agent used in combination in a phase I/II clinical trial. Patient outcome is characterized as a vector of two ordinal variables accounting for toxicity and treatment efficacy. A generalization of the Aranda-Ordaz model (1981, Biometrika 68, 357[ndash]363) is used for the marginal outcome probabilities as functions of a dose pair, and a Gaussian copula is assumed to obtain joint distributions. Numerical utilities of all elementary patient outcomes, allowing the possibility that efficacy is inevalua...
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: Nadine Houede, Peter F. Thall, Hoang Nguyen, Xavier Paoletti, Andrew Kramar Source Type: journals

A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical TrialsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
A surrogate marker (S) is a variable that can be measured earlier and often more easily than the true endpoint (T) in a clinical trial. Most previous research has been devoted to developing surrogacy measures to quantify how well S can replace T or examining the use of S in predicting the effect of a treatment (Z). However, the research often requires one to fit models for the distribution of T given S and Z. It is well known that such models do not have causal interpretations because the models condition on a postrandomization variable S. In this article, we directly model the relationship among T, S, and Z using a potent...
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: Yun Li, Jeremy M.G. Taylor, Michael R. Elliott Source Type: journals

Partial-Likelihood Analysis of Spatio-Temporal Point-Process DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We investigate the use of a partial likelihood for estimation of the parameters of interest in spatio-temporal point-process models. We identify an important distinction between spatially discrete and spatially continuous models. We focus our attention on the spatially continuous case, which has not previously been considered. We use an inhomogeneous Poisson process and an infectious disease process, for which maximum-likelihood estimation is tractable, to assess the relative efficiency of partial versus full likelihood, and to illustrate the relative ease of implementation of the former. We apply the partial-likelihood me...
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: Peter J. Diggle, Irene Kaimi, Rosa Abellana Source Type: journals

Joint Spatial Modeling of Recurrent Infection and Growth with Processes under Intermittent ObservationEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In this article, we present a new statistical methodology for longitudinal studies in forestry, where trees are subject to recurrent infection, and the hazard of infection depends on tree growth over time. Understanding the nature of this dependence has important implications for reforestation and breeding programs. Challenges arise for statistical analysis in this setting with sampling schemes leading to panel data, exhibiting dynamic spatial variability, and incomplete covariate histories for hazard regression. In addition, data are collected at a large number of locations, which poses computational difficulties for spat...
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: F. S. Nathoo Source Type: journals

Longitudinal Studies of Binary Response Data Following Case–Control and Stratified Case–Control Sampling: Design and AnalysisEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We discuss design and analysis of longitudinal studies after case[ndash]control sampling, wherein interest is in the relationship between a longitudinal binary response that is related to the sampling (case[ndash]control) variable, and a set of covariates. We propose a semiparametric modeling framework based on a marginal longitudinal binary response model and an ancillary model for subjects' case[ndash]control status. In this approach, the analyst must posit the population prevalence of being a case, which is then used to compute an offset term in the ancillary model. Parameter estimates from this model are used to comput...
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: Jonathan S. Schildcrout, Paul J. Rathouz Source Type: journals

Incorporating Correlation for Multivariate Failure Time Data When Cluster Size Is LargeEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We propose a new estimation method for multivariate failure time data using the quadratic inference function (QIF) approach. The proposed method efficiently incorporates within-cluster correlations. Therefore, it is more efficient than those that ignore within-cluster correlation. Furthermore, the proposed method is easy to implement. Unlike the weighted estimating equations in Cai and Prentice (1995, Biometrika 82, 151[ndash]164), it is not necessary to explicitly estimate the correlation parameters. This simplification is particularly useful in analyzing data with large cluster size where it is difficult to estimate intr...
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: L. Xue, L. Wang, A. Qu Source Type: journals

Joint Inference on HIV Viral Dynamics and Immune Suppression in Presence of Measurement ErrorsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In an attempt to provide a tool to assess antiretroviral therapy and to monitor disease progression, this article studies association of human immunodeficiency virus (HIV) viral suppression and immune restoration. The data from a recent acquired immune deficiency syndrome (AIDS) study are used for illustration. We jointly model HIV viral dynamics and time to decrease in CD4/CD8 ratio in the presence of CD4 process with measurement errors, and estimate the model parameters simultaneously via a method based on a Laplace approximation and the commonly used Monte Carlo EM algorithm. The approaches and many of the points presen...
Source: Biometrics - August 9, 2009 Category: Biotechnology Authors: L. Wu, W. Liu, X. J. Hu Source Type: journals

Regression Analysis with a Misclassified Covariate from a Current Status Observation SchemeEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Naive use of misclassified covariates leads to inconsistent estimators of covariate effects in regression models. A variety of methods have been proposed to address this problem including likelihood, pseudo-likelihood, estimating equation methods, and Bayesian methods, with all of these methods typically requiring either internal or external validation samples or replication studies. We consider a problem arising from a series of orthopedic studies in which interest lies in examining the effect of a short-term serological response and other covariates on the risk of developing a longer term thrombotic condition called deep...
Source: Biometrics - July 23, 2009 Category: Biotechnology Authors: Leilei Zeng, Richard J. Cook, Theodore E. Warkentin Source Type: journals

A Global Sensitivity Test for Evaluating Statistical Hypotheses with Nonidentifiable ModelsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We consider the problem of evaluating a statistical hypothesis when some model characteristics are nonidentifiable from observed data. Such a scenario is common in meta-analysis for assessing publication bias and in longitudinal studies for evaluating a covariate effect when dropouts are likely to be nonignorable. One possible approach to this problem is to fix a minimal set of sensitivity parameters conditional upon which hypothesized parameters are identifiable. Here, we extend this idea and show how to evaluate the hypothesis of interest using an infimum statistic over the whole support of the sensitivity parameter. We ...
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: D. Todem, J. Fine, L. Peng Source Type: journals

Hierarchical and Joint Site-Edge Methods for Medicare Hospice Service Region Boundary AnalysisEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Hospice service offers a convenient and ethically preferable health-care option for terminally ill patients. However, this option is unavailable to patients in remote areas not served by any hospice system. In this article, we seek to determine the service areas of two particular cancer hospice systems in northeastern Minnesota based only on death counts abstracted from Medicare billing records. The problem is one of spatial boundary analysis, a field that appears statistically underdeveloped for irregular areal (lattice) data, even though most publicly available human health data are of this type. In this article, we sugg...
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: Haijun Ma, Bradley P. Carlin, Sudipto Banerjee Source Type: journals

Controlling False Discoveries in Multidimensional Directional Decisions, with Applications to Gene Expression Data on Ordered CategoriesEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Microarray gene expression studies over ordered categories are routinely conducted to gain insights into biological functions of genes and the underlying biological processes. Some common experiments are time-course/dose-response experiments where a tissue or cell line is exposed to different doses and/or durations of time to a chemical. A goal of such studies is to identify gene expression patterns/profiles over the ordered categories. This problem can be formulated as a multiple testing problem where for each gene the null hypothesis of no difference between the successive mean gene expressions is tested and further dire...
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: Wenge Guo, Sanat K. Sarkar, Shyamal D. Peddada Source Type: journals

Forecasting Pollen Concentration by a Two-Step Functional ModelEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
A functional regression model to forecast the cypress pollen concentration during a given time interval, considering the air temperature in a previous interval as the input, is derived by means of a two-step procedure. This estimation is carried out by functional principal component (FPC) analysis and the residual noise is also modeled by FPC regression, taking as the explicative process the pollen concentration during the earlier interval. The prediction performance is then tested on pollen data series recorded in Granada (Spain) over a period of 10 years. (Source: Biometrics)
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: Mariano J. Valderrama, Francisco A. Ocaña, Ana M. Aguilera, Francisco M. Ocaña-Peinado Source Type: journals

A Bayesian Chi-Squared Goodness-of-Fit Test for Censored Data ModelsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We propose a Bayesian chi-squared model diagnostic for analysis of data subject to censoring. The test statistic has the form of Pearson's chi-squared test statistic and is easy to calculate from standard output of Markov chain Monte Carlo algorithms. The key innovation of this diagnostic is that it is based only on observed failure times. Because it does not rely on the imputation of failure times for observations that have been censored, we show that under heavy censoring it can have higher power for detecting model departures than a comparable test based on the complete data. In a simulation study, we show that tests ba...
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: Jing Cao, Ann Moosman, Valen E. Johnson Source Type: journals

Regression Calibration in Semiparametric Accelerated Failure Time ModelsEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error-prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method (Prentice, 1982, Biometrika 69, 331[ndash]342) is a popular method for analyzing such data and has been applied to the Cox model by Wang et al. (1997, Biometrics 53, 131[ndash]145) under normal measurement error and rare disease assumptions. In this article, we consider the RC estimation method for the semiparametric accelerated failu...
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: Menggang Yu, Bin Nan Source Type: journals

Incorporating Predictor Network in Penalized Regression with Application to Microarray DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We consider penalized linear regression, especially for "large p, small n" problems, for which the relationships among predictors are described a priori by a network. A class of motivating examples includes modeling a phenotype through gene expression profiles while accounting for coordinated functioning of genes in the form of biological pathways or networks. To incorporate the prior knowledge of the similar effect sizes of neighboring predictors in a network, we propose a grouped penalty based on the L[gamma]-norm that smoothes the regression coefficients of the predictors over the network. The main feature of the propos...
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: Wei Pan, Benhuai Xie, Xiaotong Shen Source Type: journals

Risk-Group-Specific Dose Finding Based on an Average Toxicity ScoreEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
We propose a Bayesian dose-finding design that accounts for two important factors, the severity of toxicity and heterogeneity in patients' susceptibility to toxicity. We consider toxicity outcomes with various levels of severity and define appropriate scores for these severity levels. We then use a multinomial-likelihood function and a Dirichlet prior to model the probabilities of these toxicity scores at each dose, and characterize the overall toxicity using an average toxicity score (ATS) parameter. To address the issue of heterogeneity in patients' susceptibility to toxicity, we categorize patients into different risk g...
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: B. Nebiyou Bekele, Yisheng Li, Yuan Ji Source Type: journals

Statistical Metrics for Quality Assessment of High-Density Tiling Array DataEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
High-density tiling arrays are designed to blanket an entire genomic region of interest using tiled oligonucleotides at very high resolution and are widely used in various biological applications. Experiments are usually conducted in multiple stages, in which unwanted technical variations may be introduced. As tiling arrays become more popular and are adopted by many research labs, it is pressing to develop quality control tools as was done for expression microarrays. We propose a set of statistical quality metrics analogous to those in expression microarrays with application to tiling array data. We also develop a method ...
Source: Biometrics - July 22, 2009 Category: Biotechnology Authors: Hui Tang, Terry M. Therneau Source Type: journals

Capture–Recapture Estimation Using Finite Mixtures of Arbitrary DimensionEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Reversible jump Markov chain Monte Carlo (RJMCMC) methods are used to fit Bayesian capture[ndash]recapture models incorporating heterogeneity in individuals and samples. Heterogeneity in capture probabilities comes from finite mixtures and/or fixed sample effects allowing for interactions. Estimation by RJMCMC allows automatic model selection and/or model averaging. Priors on the parameters stabilize the estimates and produce realistic credible intervals for population size for overparameterized models, in contrast to likelihood-based methods. To demonstrate the approach we analyze the standard Snowshoe hare and Cottontail...
Source: Biometrics - June 24, 2009 Category: Biotechnology Authors: Richard Arnold, Yu Hayakawa, Paul Yip Source Type: journals

Linear Mixed Model Selection for False Discovery Rate Control in Microarray Data AnalysisEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
In a microarray experiment, one experimental design is used to obtain expression measures for all genes. One popular analysis method involves fitting the same linear mixed model for each gene, obtaining gene-specific p-values for tests of interest involving fixed effects, and then choosing a threshold for significance that is intended to control false discovery rate (FDR) at a desired level. When one or more random factors have zero variance components for some genes, the standard practice of fitting the same full linear mixed model for all genes can result in failure to control FDR. We propose a new method that combines r...
Source: Biometrics - June 11, 2009 Category: Biotechnology Authors: Cumhur Yusuf Demirkale, Dan Nettleton, Tapabrata Maiti Source Type: journals

Order-Restricted Semiparametric Inference for the Power Bias ModelEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
The power bias model, a generalization of length-biased sampling, is introduced and investigated in detail. In particular, attention is focused on order-restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form. Estimation and testing procedures under constraints are developed in detail. It is shown that the power bias model can be used for testing for, or against, the likelihood ratio ordering among multiple populations w...
Source: Biometrics - June 11, 2009 Category: Biotechnology Authors: Ori Davidov, Konstantinos Fokianos, George Iliopoulos Source Type: journals

Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox ModelEmail this article to a colleague. Save this article to My Clippings. Discuss or comment on this article.
Length-biased time-to-event data are commonly encountered in applications ranging from epidemiological cohort studies or cancer prevention trials to studies of labor economy. A longstanding statistical problem is how to assess the association of risk factors with survival in the target population given the observed length-biased data. In this article, we demonstrate how to estimate these effects under the semiparametric Cox proportional hazards model. The structure of the Cox model is changed under length-biased sampling in general. Although the existing partial likelihood approach for left-truncated data can be used to es...
Source: Biometrics - June 11, 2009 Category: Biotechnology Authors: Jing Qin, Yu Shen Source Type: journals