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Articles 31 - 53 of 53
Full-Text Articles in Biostatistics
Semiparametric Bayesian Modeling Of Multivariate Average Bioequivalence, Pulak Ghosh Dr., Mithat Gonen
Semiparametric Bayesian Modeling Of Multivariate Average Bioequivalence, Pulak Ghosh Dr., Mithat Gonen
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
Bioequivalence trials are usually conducted to compare two or more formulations of a drug. Simultaneous assessment of bioequivalence on multiple endpoints is called multivariate bioequivalence. Despite the fact that some tests for multivariate bioequivalence are suggested, current practice usually involves univariate bioequivalence assessments ignoring the correlations between the endpoints such as AUC and Cmax. In this paper we develop a semiparametric Bayesian test for bioequivalence under multiple endpoints. Specifically, we show how the correlation between the endpoints can be incorporated in the analysis and how this correlation affects the inference. Resulting estimates and posterior probabilities ``borrow strength'' from one another …
Combining Information From Two Surveys To Estimate County-Level Prevalence Rates Of Cancer Risk Factors And Screening, Trivellore E. Raghuanthan, Dawei Xie, Nathaniel Schenker, Van Parsons, William W. Davis, Kevin W. Dodd, Eric J. Feuer
Combining Information From Two Surveys To Estimate County-Level Prevalence Rates Of Cancer Risk Factors And Screening, Trivellore E. Raghuanthan, Dawei Xie, Nathaniel Schenker, Van Parsons, William W. Davis, Kevin W. Dodd, Eric J. Feuer
The University of Michigan Department of Biostatistics Working Paper Series
Cancer surveillance requires estimates of the prevalence of cancer risk factors and screening for small areas such as counties. Two popular data sources are the Behavioral Risk Factor Surveillance System (BRFSS), a telephone survey conducted by state agencies, and the National Health Interview Survey (NHIS), an area probability sample survey conducted through face-to-face interviews. Both data sources have advantages and disadvantages. The BRFSS is a larger survey, and almost every county is included in the survey; but it has lower response rates as is typical with telephone surveys, and it does not include subjects who live in households with no …
Semiparametric Latent Variable Regression Models For Spatio-Temporal Modeling Of Mobile Source Particles In The Greater Boston Area, Alexandros Gryparis, Brent A. Coull, Joel Schwartz, Helen H. Suh
Semiparametric Latent Variable Regression Models For Spatio-Temporal Modeling Of Mobile Source Particles In The Greater Boston Area, Alexandros Gryparis, Brent A. Coull, Joel Schwartz, Helen H. Suh
Harvard University Biostatistics Working Paper Series
Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic …
Empirical Bayes Approach To Controlling Familywise Error: An Application To Hiv Resistance Data, Rhoderick N. Machekano, Alan E. Hubbard
Empirical Bayes Approach To Controlling Familywise Error: An Application To Hiv Resistance Data, Rhoderick N. Machekano, Alan E. Hubbard
U.C. Berkeley Division of Biostatistics Working Paper Series
Statistical challenges arise in identifying meaningful patterns and structures from high dimensional genomic data sets. Relating HIV genotype (sequence of amino acids) to phenotypic resistance presents a typical problem. When the HIV virus is under antiretroviral drug pressure, unfavorable mutations of the target genes often lead to greatly increased resistance of the virus to drugs, including drugs the virus has not been exposed to. Identification of mutation combinations and their correlation to drug resistance is critical in guiding efficient prescription of HIV drugs. The identification of a subset of codons associated with drug resistance from a set of several hundreds …
Reliability, Effect Size, And Responsiveness And Intraclass Correlation Of Health Status Measures Used In Randomized And Cluster-Randomized Trials, Paula Diehr, Lu Chen, Donald L. Patrick, Ziding Feng, Yutaka Yasui
Reliability, Effect Size, And Responsiveness And Intraclass Correlation Of Health Status Measures Used In Randomized And Cluster-Randomized Trials, Paula Diehr, Lu Chen, Donald L. Patrick, Ziding Feng, Yutaka Yasui
UW Biostatistics Working Paper Series
Background: New health status instruments are described by psychometric properties, such as Reliability, Effect Size, and Responsiveness. For cluster-randomized trials, another important statistic is the Intraclass Correlation for the instrument within clusters. Studies using better instruments can be performed with smaller sample sizes, but better instruments may be more expensive in terms of dollars, lost opportunities, or poorer data quality due to the response burden of longer instruments. Investigators often need to estimate the psychometric properties of a new instrument, or of an established instrument in a new setting. Optimal sample sizes for estimating these properties have not been studied …
Detecting Pulsatile Hormone Secretion Events: A Bayesian Approach, Tim Johnson
Detecting Pulsatile Hormone Secretion Events: A Bayesian Approach, Tim Johnson
The University of Michigan Department of Biostatistics Working Paper Series
Many challenges arise in the analysis of pulsatile, or episodic, hormone concentration time series data. Among these challenges is the determination of the number and location of pulsatile events and the discrimination of events from noise. Analyses of these data are typically performed in two stages. In the first stage, the number and approximate location of the pulses are determined. In the second stage, a model (typically a deconvolution model) is fit to the data conditional on the number of pulses. Any error made in the first stage is carried over to the second stage. Furthermore, current methods, except two, …
Semiparametric Analysis For Correlated Recurrent And Terminal Events, Yining Ye, Jack Kalbfleisch, Doug E. Schaubel
Semiparametric Analysis For Correlated Recurrent And Terminal Events, Yining Ye, Jack Kalbfleisch, Doug E. Schaubel
The University of Michigan Department of Biostatistics Working Paper Series
In clinical and observational studies, recurrent event data (e.g. hospitalization) with a terminal event (e.g. death) are often encountered. In many instances, the terminal event is strongly correlated with the recurrent event process. In this article, we propose a semiparametric method to jointly model the recurrent and terminal event processes. The dependence is modeled by a shared gamma frailty that is included in both the recurrent event rate and terminal event hazard function. Marginal models are used to estimate the regression effects on the terminal and recurrent event processes and a Poisson model is used to estimate the dispersion of …
Adjusting For Covariate Effects On Classification Accuracy Using The Covariate-Adjusted Roc Curve, Holly Janes, Margaret S. Pepe
Adjusting For Covariate Effects On Classification Accuracy Using The Covariate-Adjusted Roc Curve, Holly Janes, Margaret S. Pepe
UW Biostatistics Working Paper Series
Recent scientific and technological innovations have produced an abundance of potential markers which are being investigated for their use in disease screen- ing and diagnosis. In evaluating these markers, it is often necessary to account for covariates which are associated with the marker of interest. These covariates may include subject characteristics, expertise of the test operator, test proce- dures, or aspects of specimen handling. In this paper, we propose the AROC, a covariate-adjusted measure of the classification accuracy. The AROC is the common covariate-specific ROC curve, when the covariate does not affect dis- crimination, and a weighted average of covariate-specific …
Genome Scanning Methods For Comparing Sequences Between Groups, With Application To Hiv Vaccine Trials, Peter B. Gilbert, Chunyuan Wu, David V. Jobes
Genome Scanning Methods For Comparing Sequences Between Groups, With Application To Hiv Vaccine Trials, Peter B. Gilbert, Chunyuan Wu, David V. Jobes
UW Biostatistics Working Paper Series
Consider a placebo-controlled preventive HIV vaccine efficacy trial. An HIV amino acid sequence is measured from each volunteer who acquires HIV, and these sequences are aligned together with the reference HIV sequence represented in the vaccine. We develop genome scanning methods to identify HIV positions at which the amino acids in sequences from infected vaccine recipients tend to be more divergent from the corresponding reference amino acid than the amino acids in sequences from infected placebo recipients. We consider five two-sample test statistics, based on Euclidean, Mahalanobis, and Kullback-Leibler divergence measures. Weights are incorporated to reflect biological information contained in …
Multiple Tests Of Association With Biological Annotation Metadata, Sandrine Dudoit, Sunduz Keles, Mark J. Van Der Laan
Multiple Tests Of Association With Biological Annotation Metadata, Sandrine Dudoit, Sunduz Keles, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We propose a general and formal statistical framework for the multiple tests of associations between known fixed features of a genome and unknown parameters of the distribution of variable features of this genome in a population of interest. The known fixed gene-annotation profiles, corresponding to the fixed features of the genome, may concern Gene Ontology (GO) annotation, pathway membership, regulation by particular transcription factors, nucleotide sequences, or protein sequences. The unknown gene-parameter profiles, corresponding to the variable features of the genome, may be, for example, regression coefficients relating genome-wide transcript levels or DNA copy numbers to possibly censored biological and …
Evaluating The Predictiveness Of A Continuous Marker, Ying Huang, Margaret S. Pepe, Ziding Feng
Evaluating The Predictiveness Of A Continuous Marker, Ying Huang, Margaret S. Pepe, Ziding Feng
UW Biostatistics Working Paper Series
Consider a continuous marker for predicting a binary outcome. For example, serum concentration of prostate specific antigen (PSA) may be used to calculate the risk of finding prostate cancer in a biopsy. In this paper we argue that the predictive capacity of a marker has to do with the population distribution of risk given the marker and suggest a graphical tool, the predictiveness curve, that displays this distribution. The display provides a common meaningful scale for comparing markers that may not be comparable on their original scales. Some existing measures of predictiveness are shown to be summary indices derived from …
Comparing The Predictive Values Of Diagnostic Tests: Sample Size And Analysis For Paired Study Designs, Chaya S. Moskowitz, Margaret S. Pepe
Comparing The Predictive Values Of Diagnostic Tests: Sample Size And Analysis For Paired Study Designs, Chaya S. Moskowitz, Margaret S. Pepe
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
In this paper we consider the design and analysis of studies comparing the positive and negative predictive values of two diagnostic tests that are measured on all subjects. Although statistical methodology is well developed for comparing diagnostic tests in terms of their sensitivities and specificities, comparative inference about predictive values is not. We derive analytic variance expressions for the relative predictive values. Sample size formulas for study design ensue. In addition, two new methods for analyzing the resulting data are presented and compared with an existing marginal regression methodology.
Regression Analysis For The Partial Area Under The Roc Curve, Tianxi Cai, Lori E. Dodd
Regression Analysis For The Partial Area Under The Roc Curve, Tianxi Cai, Lori E. Dodd
Harvard University Biostatistics Working Paper Series
No abstract provided.
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Dartmouth Scholarship
The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.
Comparison Of Haplotype-Based And Tree-Based Snp Imputation In Association Studies, James Y. Dai, Ingo Ruczinski, Michael Leblanc, Charles Kooperberg
Comparison Of Haplotype-Based And Tree-Based Snp Imputation In Association Studies, James Y. Dai, Ingo Ruczinski, Michael Leblanc, Charles Kooperberg
UW Biostatistics Working Paper Series
Missing single nucleotide polymorphisms (SNPs) are quite common in genetic association studies. Subjects with missing SNPs are often discarded in analyses, which may seriously undermine the inference of SNP-disease association. In this article, we compare two haplotype-based imputation approaches and one regression tree-based imputation approach for association studies. The goal is to assess the imputation accuracy, and to evaluate the impact of imputation on parameter estimation. Haplotype-based approaches build on haplotype reconstruction by the expectation-maximization (EM) algorithm or a weighted EM (WEM) algorithm, depending on whether case-control status is taken into account. The tree-based approach uses a Gibbs sampler to …
Quantifying The Effects Of Correlated Covariates On Variable Importance Estimates From Random Forests, Ryan Vincent Kimes
Quantifying The Effects Of Correlated Covariates On Variable Importance Estimates From Random Forests, Ryan Vincent Kimes
Theses and Dissertations
Recent advances in computing technology have lead to the development of algorithmic modeling techniques. These methods can be used to analyze data which are difficult to analyze using traditional statistical models. This study examined the effectiveness of variable importance estimates from the random forest algorithm in identifying the true predictor among a large number of candidate predictors. A simulation study was conducted using twenty different levels of association among the independent variables and seven different levels of association between the true predictor and the response. We conclude that the random forest method is an effective classification tool when the goals …
Assessing, Modifying, And Combining Data Fields From The Virginia Office Of The Chief Medical Examiner (Ocme) Dataset And The Virginia Department Of Forensic Science (Dfs) Datasets In Order To Compare Concentrations Of Selected Drugs, Amy Elizabeth Herrin
Theses and Dissertations
The Medical Examiner of Virginia (ME) dataset and the Virginia Department of Forensic Science Driving Under the Influence of Drugs (DUI) datasets were used to determine whether people have the potential to develop tolerances to diphenhydramine, cocaine, oxycodone, hydrocodone, methadone, and morphine. These datasets included the years 2000-2004 and were used to compare the concentrations of these six drugs between people who died from a drug-related cause of death (of the drug of interest) and people who were pulled over for driving under the influence. Three drug pattern groups were created to divide each of the six drug-specific datasets in …
Optimal Clustering: Genetic Constrained K-Means And Linear Programming Algorithms, Jianmin Zhao
Optimal Clustering: Genetic Constrained K-Means And Linear Programming Algorithms, Jianmin Zhao
Theses and Dissertations
Methods for determining clusters of data under- specified constraints have recently gained popularity. Although general constraints may be used, we focus on clustering methods with the constraint of a minimal cluster size. In this dissertation, we propose two constrained k-means algorithms: Linear Programming Algorithm (LPA) and Genetic Constrained K-means Algorithm (GCKA). Linear Programming Algorithm modifies the k-means algorithm into a linear programming problem with constraints requiring that each cluster have m or more subjects. In order to achieve an acceptable clustering solution, we run the algorithm with a large number of random sets of initial seeds, and choose the solution …
A Comparison For Longitudinal Data Missing Due To Truncation, Rong Liu
A Comparison For Longitudinal Data Missing Due To Truncation, Rong Liu
Theses and Dissertations
Many longitudinal clinical studies suffer from patient dropout. Often the dropout is nonignorable and the missing mechanism needs to be incorporated in the analysis. The methods handling missing data make various assumptions about the missing mechanism, and their utility in practice depends on whether these assumptions apply in a specific application. Ramakrishnan and Wang (2005) proposed a method (MDT) to handle nonignorable missing data, where missing is due to the observations exceeding an unobserved threshold. Assuming that the observations arise from a truncated normal distribution, they suggested an EM algorithm to simplify the estimation.In this dissertation the EM algorithm is …
Statistical Methods And Experimental Design For Inference Regarding Dose And/Or Interaction Thresholds Along A Fixed-Ratio Ray, Sharon Dziuba Yeatts
Statistical Methods And Experimental Design For Inference Regarding Dose And/Or Interaction Thresholds Along A Fixed-Ratio Ray, Sharon Dziuba Yeatts
Theses and Dissertations
An alternative to the full factorial design, the ray design is appropriate for investigating a mixture of c chemicals, which are present according to a fixed mixing ratio, called the mixture ray. Using single chemical and mixture ray data, we can investigate interaction among the chemicals in a particular mixture. Statistical models have been used to describe the dose-response relationship of the single agents and the mixture; additivity is tested through the significance of model parameters associated with the coincidence of the additivity and mixture models.It is often assumed that a chemical or mixture must be administered above an unknown …
A Normal-Mixture Model With Random-Effects For Rr-Interval Data, Jessica Mckinney Ketchum
A Normal-Mixture Model With Random-Effects For Rr-Interval Data, Jessica Mckinney Ketchum
Theses and Dissertations
In many applications of random-effects models to longitudinal data, such as heart rate variability (HRV) data, a normal-mixture distribution seems to be more appropriate than the normal distribution assumption. While the random-effects methodology is well developed for several distributions in the exponential family, the case of the normal-mixture has not been dealt with adequately in the literature. The models and the estimation methods that have been proposed in the past assume the conditional model (fixing the random-effects) to be normal and allow a mixture distribution for the random effects (Xu and Hedeker, 2001, Xu, 1995). The methods proposed in this …
Meta-Analysis Of Open Vs Closed Surgery Of Mandibular Condyle Fractures, Marcy Lauren Nussbaum
Meta-Analysis Of Open Vs Closed Surgery Of Mandibular Condyle Fractures, Marcy Lauren Nussbaum
Theses and Dissertations
A review of the literature reveals a difference of opinion regarding whether open or closed reduction of condylar fractures produces the best results. It would be beneficial to critically analyze past studies that have directly compared the two methods in an attempt to answer this question. A Medline search for articles using the key words 'mandibular condyle fractures' and 'mandibular condyle fractures surgery' was performed. The articles chosen for the meta-analysis contained data on at least one of the following: postoperative maximum mouth opening, lateral excursion, protrusion, deviation on opening, asymmetry, and joint pain or muscle pain. Several common statistical …
Design And Analysis Methods For Cluster Randomized Trials With Pair-Matching On Baseline Outcome: Reduction Of Treatment Effect Variance, Misook Park
Theses and Dissertations
Cluster randomized trials (CRT) are comparative studies designed to evaluate interventions where the unit of analysis and randomization is the cluster but the unit of observation is individuals within clusters. Typically such designs involve a limited number of clusters and thus the variation between clusters is left uncontrolled. Experimental designs and analysis strategies that minimize this variance are required. In this work we focus on the CRT with pre-post intervention measures. By incorporating the baseline measure into the analysis, we can effectively reduce the variance of the treatment effect. Well known methods such as adjustment for baseline as a covariate …