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Articles 31 - 52 of 52
Full-Text Articles in Biostatistics
Estimation Based On Case-Control Designs With Known Incidence Probability, Mark J. Van Der Laan
Estimation Based On Case-Control Designs With Known Incidence Probability, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Case-control sampling is an extremely common design used to generate data to estimate effects of exposures or treatments on a binary outcome of interest when the proportion of cases (i.e., binary outcome equal to 1) in the population of interest is low. Case-control sampling represents a biased sample of a target population of interest by sampling a disproportional number of cases. Case-control studies are also commonly employed to estimate the effects of genetic markers or biomarkers on phenotypes. The typical approach used in practice is to fit (conditional) logistic regression models, ignoring the case-control sampling, in order to estimate the …
A Guide To Causal Parameters In Case-Control Designs: Targeted Maximum Likelihood Estimation, Sherri Rose, Mark J. Van Der Laan
A Guide To Causal Parameters In Case-Control Designs: Targeted Maximum Likelihood Estimation, Sherri Rose, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Researchers of uncommon diseases are often interested in assessing potential risk factors. Given the low incidence of disease, these studies are frequently case-control in design, as this allows for a sufficient number of cases to be obtained without extensive sampling and can increase efficiency. However, these case-control samples are then biased since the proportion of cases in the sample is not the same as the population of interest. Methods for analyzing case-control studies have focused on utilizing logistic regression models that provide conditional and not causal estimates of the odds ratio. This article will demonstrate the use of the prevalence …
Semiparametric Methods For Evaluating The Covariate-Specific Predictiveness Of Continuous Markers In Matched Case-Control Studies, Ying Huang, Margaret S. Pepe
Semiparametric Methods For Evaluating The Covariate-Specific Predictiveness Of Continuous Markers In Matched Case-Control Studies, Ying Huang, Margaret S. Pepe
UW Biostatistics Working Paper Series
To assess the value of a continuous marker in predicting the risk of a disease, a graphical tool called the predictiveness curve has been proposed. It characterizes the marker's predictiveness, or capacity to risk stratify the population by displaying the population distribution of risk endowed by the marker. Methods for making inference about the curve and for comparing curves in a general population have been developed. However, knowledge about a marker's performance in the general population only is not enough. Since a marker's effect on the risk model and its distribution can both differ across subpopulations, its predictiveness may vary …
Nonparametric Heteroscedastic Transformation Regression Models For Skewed Data With An Application To Health Care Costs, Xiao-Hua Zhou, Huazhen Lin, Eric Johnson
Nonparametric Heteroscedastic Transformation Regression Models For Skewed Data With An Application To Health Care Costs, Xiao-Hua Zhou, Huazhen Lin, Eric Johnson
UW Biostatistics Working Paper Series
No abstract provided.
Semiparametric Inferential Procedures For Comparing Multivariate Roc Curves With Interaction Terms, Liansheng Tang, Xiao-Hua Zhou
Semiparametric Inferential Procedures For Comparing Multivariate Roc Curves With Interaction Terms, Liansheng Tang, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
Multivariate ROC curve models that include an interaction term be- tween biomarker type and false positive rate is important in comparative biomarker studies, because such interaction allows ROC curves of different biomarkers to cross each other. However, there has been limited work in drawing inference for comparing multivariate ROC curves, especially when the interaction terms are present. In this article we derive the asymptotic covariance of three estimators for multivariate ROC models. These covariance estimates have not been readily available in the literature, and bootstrap methods have to be used to obtain co- variance estimates. With the readily available variance …
Matrix Pooling: An Accurate And Cost Effective Testing Algorithm For Detection Of Acute Hiv Infection, Bethany L. Hedt, Marcello Pagano
Matrix Pooling: An Accurate And Cost Effective Testing Algorithm For Detection Of Acute Hiv Infection, Bethany L. Hedt, Marcello Pagano
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semi-Parametric Maximum Likelihood Estimates For Roc Curves Of Continuous-Scale Tests, Xiao-Hua Zhou, Huazhen Lin
Semi-Parametric Maximum Likelihood Estimates For Roc Curves Of Continuous-Scale Tests, Xiao-Hua Zhou, Huazhen Lin
UW Biostatistics Working Paper Series
No abstract provided.
A Matrix Pooling Algorithm For Disease Detection, Bethany L. Hedt, Marcello Pagano
A Matrix Pooling Algorithm For Disease Detection, Bethany L. Hedt, Marcello Pagano
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Inference Procedure For Percentiles Of The Random Effect Distribution In Meta Analysis, Rui Wang, Lu Tian, Tianxi Cai, L. J. Wei
Nonparametric Inference Procedure For Percentiles Of The Random Effect Distribution In Meta Analysis, Rui Wang, Lu Tian, Tianxi Cai, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Bayesian Approach To Modeling Associations Between Pulsatile Hormones, Nichole E. Carlson, Timothy D. Johnson, Morton B. Brown
A Bayesian Approach To Modeling Associations Between Pulsatile Hormones, Nichole E. Carlson, Timothy D. Johnson, Morton B. Brown
The University of Michigan Department of Biostatistics Working Paper Series
Many hormones are secreted in pulses. The pulsatile relationship between hormones regulates many biological processes. To understand endocrine system regulation, time series of hormone concentrations are collected. The goal is to characterize pulsatile patterns and associations between hormones. Currently each hormone on each subject is fitted univariately. This leads to estimates of the number of pulses and estimates of the amount of hormone secreted; however, when the signal-to-noise ratio is small, pulse detection and parameter estimation remains di±cult with existing approaches. In this paper, we present a bivariate deconvolution model of pulsatile hormone data focusing on incorporating pulsatile associations. Through …
Caffeine Model Identification For Vigilance Performance Prediction, Chun-Hui Huang
Caffeine Model Identification For Vigilance Performance Prediction, Chun-Hui Huang
Mechanical & Aerospace Engineering Theses & Dissertations
The pharmacodynamics and pharmacokinetics of caffeine have been well characterized. In this study, a caffeine dynamic model is developed to describe its pharmacodynamic effects on vigilance performance. Validated biomathematical models developed to address both individual and group fatigue and alertness in a non-laboratory setting represent a tremendous commercial opportunity. First, a test data set with caffeine effects isolated from circadian and homeostatic effects is created. Then a modeling approach for input and output effects is developed and different model structures for the caffeine effects are considered. Observer/Kalman filter Identification (OKID) algorithm is proposed and developed to identify the caffeine model …
Targeted Methods For Biomarker Discovery, The Search For A Standard, Catherine Tuglus, Mark J. Van Der Laan
Targeted Methods For Biomarker Discovery, The Search For A Standard, Catherine Tuglus, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
More often than not biomarker studies analyze large quantities of variables with complicated and generally unknown correlation structure. There are numerous statistical methods which attempt to unravel these variables and determine the underlying mechanism through identification of causally related biomarkers. Results from these methods are generally difficult to interpret and nearly impossible to compare across studies. The FDA has currently called for a standardization of methods and protocol for biomarker detection. In response, we propose targeted variable importance (tVIM) as a standardized method for biomarker discovery. Through the use of targeted Maximum Likelihood, tVIM provides double robust estimates of variable …
Data-Adaptive Selection Of The Truncation Level For Inverse-Probability-Of-Treatment-Weighted Estimators, Oliver Bembom, Mark J. Van Der Laan
Data-Adaptive Selection Of The Truncation Level For Inverse-Probability-Of-Treatment-Weighted Estimators, Oliver Bembom, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Inverse-Probability-of-Treatment-Weighted (IPTW) estimators are becoming a popular analysis tool in causal inference. It is well known that these estimators suffer from high variability if some treatment probabilities are estimated to be close to zero. While it is a common recommendation for such situations to truncate the weights in order to reduce the mean squared error of the estimator, the current literature gives little guidance on how to select an appropriate truncation level. In this article, we develop a closed-form estimate for the mean squared error of a truncated IPTW estimator that can be used to select this truncation level data-adaptively. …
Data-Adaptive Selection Of The Adjustment Set In Variable Importance Estimation, Oliver Bembom, Jeffrey W. Fessel, Robert W. Shafer, Mark J. Van Der Laan
Data-Adaptive Selection Of The Adjustment Set In Variable Importance Estimation, Oliver Bembom, Jeffrey W. Fessel, Robert W. Shafer, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
If estimates of the effect of a treatment variable on an outcome of interest are to be adjusted for a set of possible confounding factors, it is necessary to rely on the assumption of experimental treatment assignment (ETA) according to which each experimental unit has positive probability of being observed at any of the possible levels of the treatment variable regardless of the values the confounding factors may take on. Even if this assumption is only practically violated in the sense that certain values of the confounding factors cause some treatment levels to become not impossible, but at least highly …
The Expression Of Microrna Mir-107 Decreases Early In Alzheimer's Disease And May Accelerate Disease Progression Through Regulation Of Β-Site Amyloid Precursor Protein-Cleaving Enzyme 1, Wang-Xia Wang, Bernard W. Rajeev, Arnold J. Stromberg, Na Ren, Guiliang Tang, Qingwei Huang, Isidore Rigoutsos, Peter T. Nelson
The Expression Of Microrna Mir-107 Decreases Early In Alzheimer's Disease And May Accelerate Disease Progression Through Regulation Of Β-Site Amyloid Precursor Protein-Cleaving Enzyme 1, Wang-Xia Wang, Bernard W. Rajeev, Arnold J. Stromberg, Na Ren, Guiliang Tang, Qingwei Huang, Isidore Rigoutsos, Peter T. Nelson
Sanders-Brown Center on Aging Faculty Publications
MicroRNAs (miRNAs) are small regulatory RNAs that participate in posttranscriptional gene regulation in a sequence-specific manner. However, little is understood about the role(s) of miRNAs in Alzheimer's disease (AD). We used miRNA expression microarrays on RNA extracted from human brain tissue from the University of Kentucky Alzheimer's Disease Center Brain Bank with near-optimal clinicopathological correlation. Cases were separated into four groups: elderly nondemented with negligible AD-type pathology, nondemented with incipient AD pathology, mild cognitive impairment (MCI) with moderate AD pathology, and AD. Among the AD-related miRNA expression changes, miR-107 was exceptional because miR-107 levels decreased significantly even in patients with …
Rna Genome Annotation With A Focus On T. Brucei, Brett Bucci
Rna Genome Annotation With A Focus On T. Brucei, Brett Bucci
Theses
The goal of this project is to identify untranslated regions (UTRs) and UTR-indicating patterns in the genome of T. brucei. T. brucei is an interesting organism, and as the cause of African sleeping sickness -- which infects 300,000-500,000 people and a significant number of cattle annually -- is currently the subject of considerable research. Using existing algorithms, several patterns have been found that may lead to more complete UTR annotations in the T. brucei genome. The most encouraging sequence is the 11-base sequence GAGGGIICG]TGGGG, which appears in five hypothetical genes near the tail. Discovery of several such sequences could guide …
Utr Prediction Programs For Trypanosoma Brucei, Maria Moutafis
Utr Prediction Programs For Trypanosoma Brucei, Maria Moutafis
Theses
In the past few years, the field of bioinformatics has seen a rapid increase in the need for use of various sequence analysis tools. As we advance in the fields of science and technology, new programs and software are constantly being developed in this field. Rapidly expanding gene sequence databases and rapidly evolving sequence analysis tools are providing researchers with ways to search for highly similar query sequences whether they are nucleotide, protein, or gene databases. This thesis will focus on sequence alignment tools, specifically concentrating on tools that help determine/predict non-coding regions of sequences also known as untranslated regions …
Cluster Mass Inference Method Via Random Field Theory, Hui Zhang, Thomas E. Nichols, Timothy D. Johnson
Cluster Mass Inference Method Via Random Field Theory, Hui Zhang, Thomas E. Nichols, Timothy D. Johnson
The University of Michigan Department of Biostatistics Working Paper Series
Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this …
Accommodating Covariates In Roc Analysis, Holly Janes, Gary M. Longton, Margaret Pepe
Accommodating Covariates In Roc Analysis, Holly Janes, Gary M. Longton, Margaret Pepe
UW Biostatistics Working Paper Series
Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized using the receiver operating characteristic (ROC) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three different ways of using covariate informa- tion. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (ie the ROC curve), we describe methods for mod- elling the ROC curve as a function of covariates. Finally, for factors …
Estimation And Comparison Of Receiver Operating Characteristic Curves, Margaret Pepe, Gary M. Longton, Holly Janes
Estimation And Comparison Of Receiver Operating Characteristic Curves, Margaret Pepe, Gary M. Longton, Holly Janes
UW Biostatistics Working Paper Series
The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Non-parametric, semiparametric and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under the ROC curve. Alternatively pointwise comparisons between ROC curves or inverse ROC curves can be made. Options to adjust these analyses for covariates, and to perform ROC regression are described in a companion article. We use a unified framework by representing the ROC curve …
On Matched Pairs Sign Test Using Bivariate Ranked Set Sampling: An Application To Environmental Issues, Hani M. Samawi, Mohammad F. Al-Saleh, Obaid Al-Saidy
On Matched Pairs Sign Test Using Bivariate Ranked Set Sampling: An Application To Environmental Issues, Hani M. Samawi, Mohammad F. Al-Saleh, Obaid Al-Saidy
Biostatistics: Faculty Publications
The matched pairs sign test using bivariate ranked set sampling (BVRSS) is introduced and investigated. We show that this test is asymptotically more efficient than its counterpart sign test based on a bivariate simple random sample (BVSRS). The asymptotic null distribution and the efficiency of the test are derived. The Pitman asymptotic relative efficiency is used to compare the asymptotic performance of the matched pairs sign test using BVRSS versus using BVSRS. For small sample sizes, the bootstrap method is used to estimate P-values. Numerical comparisons are used to gain insight about the efficiency of the BVRSS sign test compared …
Probe Level Analysis Of Affymetrix Microarray Data, Richard Ellis Kennedy
Probe Level Analysis Of Affymetrix Microarray Data, Richard Ellis Kennedy
Theses and Dissertations
The analysis of Affymetrix GeneChip® data is a complex, multistep process. Most often, methodscondense the multiple probe level intensities into single probeset level measures (such as RobustMulti-chip Average (RMA), dChip and Microarray Suite version 5.0 (MAS5)), which are thenfollowed by application of statistical tests to determine which genes are differentially expressed. An alternative approach is a probe-level analysis, which tests for differential expression directly using the probe-level data. Probe-level models offer the potential advantage of more accurately capturing sources of variation in microarray experiments. However, this has not been thoroughly investigated, since current research efforts have largely focused on the …