Statistics and Probability Commons^™

Application Of A Multiple Testing Procedure Controlling The Proportion Of False Positives To Protein And Bacterial Data, Merrill D. Birkner, Alan E. Hubbard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Simultaneously testing multiple hypotheses is important in high-dimensional biological studies. In these situations, one is often interested in controlling the Type-I error rate, such as the proportion of false positives to total rejections (TPPFP) at a specific level, alpha. This article will present an application of the E-Bayes/Bootstrap TPPFP procedure, presented in van der Laan et al. (2005), which controls the tail probability of the proportion of false positives (TPPFP), on two biological datasets. The two data applications include firstly, the application to a mass-spectrometry dataset of two leukemia subtypes, AML and ALL. The protein data measurements include intensity and …

Test Statistics Null Distributions In Multiple Testing: Simulation Studies And Applications To Genomics, Katherine S. Pollard, Merrill D. Birkner, Mark J. Van Der Laan, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying differentially expressed or co-expressed genes in microarray experiments. We have developed generally applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for control of a broad class of Type I error rates, defined as tail probabilities and expected values for arbitrary functions of the numbers of false positives and rejected hypotheses (Dudoit and van der Laan, 2005; Dudoit et al., 2004a,b; Pollard and van der Laan, 2004; van der Laan et al., 2005, 2004a,b). As argued in the early article of Pollard and van der …

Linear Regression Of Censored Length-Biased Lifetimes, Ying Qing Chen, Yan Wang

UW Biostatistics Working Paper Series

Length-biased lifetimes may be collected in observational studies or sample surveys due to biased sampling scheme. In this article, we use a linear regression model, namely, the accelerated failure time model, for the population lifetime distributions in regression analysis of the length-biased lifetimes. It is discovered that the associated regression parameters are invariant under the length-biased sampling scheme. According to this discovery, we propose the quasi partial score estimating equations to estimate the population regression parameters. The proposed methodologies are evaluated and demonstrated by simulation studies and an application to actual data set.

On Additive Regression Of Expectancy, Ying Qing Chen

UW Biostatistics Working Paper Series

Regression models have been important tools to study the association between outcome variables and their covariates. The traditional linear regression models usually specify such an association by the expectations of the outcome variables as function of the covariates and some parameters. In reality, however, interests often focus on their expectancies characterized by the conditional means. In this article, a new class of additive regression models is proposed to model the expectancies. The model parameters carry practical implication, which may allow the models to be useful in applications such as treatment assessment, resource planning or short-term forecasting. Moreover, the new model …

A Partial Likelihood For Spatio-Temporal Point Processes, Peter J. Diggle

Johns Hopkins University, Dept. of Biostatistics Working Papers

Spatio-temporal point process data arise in many fields of application. An intuitively natural way to specify a model for a spatio-temporal point process is through its conditional intensity at location x and time t, given the history of the process up to time t. Typically, this results in an analytically intractable likelihood. Likelihood-based inference therefore relies on Monte Carlo methods which are computationally intensive and require careful tuning to each application. We propose a partial likelihood alternative which is computationally straightforward and can be applied routinely. We apply the method to data from the 2001 foot-and-mouth epidemic in the UK, …

Polydesigns And Causal Inference, Fan Li, Constantine E. Frangakis

Johns Hopkins University, Dept. of Biostatistics Working Papers

In an increasingly common class of studies, the goal is to evaluate causal effects of treatments that are only partially controlled by the investigator. In such studies there are two conflicting features: (1) a model on the full cohort design and data can identify the causal effects of interest, but can be sensitive to extreme regions of that design's data, where model specification can have more impact; and (2) models on a reduced design (i.e., a subset of the full data), e.g., conditional likelihood on matched subsets of data, can avoid such sensitivity, but do not generally identify the causal …

Fit-To-Fight: Waist Vs. Waist/Height Measurements To Determine An Individual's Fitness Level A Study In Statistical Regression And Analysis, Steven J. Swiderski

Theses and Dissertations

Air Force members are to be tested for fitness by measuring their abdominal circumference, counting the number of sit-ups and push-ups they can accomplish, and the time it takes them to run 1 and miles. The abdominal measurement is a "one-size-fits-all" fitness standard. This research determines that a person's waist-to-height ratio is a better measurement than the waist measurement to estimate an individual's fitness level. This research estimates that all of the variables used to proxy fitness (Gender, Age, Height, Waist Circumference, Waist-to-Height Ratio, Push-Ups, and Sit-Ups) are statistically significant and do represent good estimators of physical fitness. This research …

A Linear Regression Framework For Receiver Operating Characteristic(Roc) Curve Analysis, Zheng Zhang, Margaret S. Pepe

UW Biostatistics Working Paper Series

In the field of medical diagnostic testing, the receiver operating characteristics(ROC) curve has long been used as a standard statistical tool to assess the accuracy of tests that yield continuous results. Although previous research in this area focused mostly on estimating the ROC curve, recently it has been recognized that the accuracy of a given test may fluctuate depending on certain factors, which motivates modelling covariate effects on the ROC curve. Comparing the corresponding ROC curves between two or more tests is a special case of covariate effect modelling. In this manuscript, we introduce a linear regression framework to model …

Structure And Dynamics Of Soluble Guanylyl Cyclase, Kentaro Sugino

Theses

Soluble guanylyl cyclase (sGC) is one of the key enzymes involved in many fundamental biological processes including vasodilatation. It can be allosterically activated by synthetic compound such as YC-l. Recently, the 3D structure of adenylyl cyclase (AC), which is a homologue of sGC, was determined. Using AC as template and homology modeling, the 3D structure of sGC is predicted. Prior experimental work has suggested two binding modes of YC- 1. In the current investigation, molecular dynamics simulations (MD) were conducted to seek more detail of molecular mechanism of sGC activation.

From these MD simulations, a tentative mechanism of sGC activation …

New Statistical Paradigms Leading To Web-Based Tools For Clinical/Translational Science, Knut M. Wittkowski

COBRA Preprint Series

As the field of functional genetics and genomics is beginning to mature, we become confronted with new challenges. The constant drop in price for sequencing and gene expression profiling as well as the increasing number of genetic and genomic variables that can be measured makes it feasible to address more complex questions. The success with rare diseases caused by single loci or genes has provided us with a proof-of-concept that new therapies can be developed based on functional genomics and genetics.

Common diseases, however, typically involve genetic epistasis, genomic pathways, and proteomic pattern. Moreover, to better understand the underlying biologi-cal …

Estimating Function Based Cross-Validation And Learning, Mark J. Van Der Laan, Daniel Rubin

U.C. Berkeley Division of Biostatistics Working Paper Series

Suppose that we observe a sample of independent and identically distributed realizations of a random variable. Given a model for the data generating distribution, assume that the parameter of interest can be characterized as the parameter value which makes the population mean of a possibly infinite dimensional estimating function equal to zero. Given a collection of candidate estimators of this parameter, and specification of the vector estimating function, we propose cross-validation criteria for selecting among these estimators. This cross-validation criteria is defined as the Euclidean norm of the empirical mean over the validation sample of the estimating function at the …

Multiple Imputation For Correcting Verification Bias, Ofer Harel, Xiao-Hua Zhou

UW Biostatistics Working Paper Series

In the case in which all subjects are screened using a common test, and only a subset of these subjects are tested using a golden standard test, it is well documented that there is a risk for bias, called verification bias. When the test has only two levels (e.g. positive and negative) and we are trying to estimate the sensitivity and specificity of the test, one is actually constructing a confidence interval for a binomial proportion. Since it is well documented that this estimation is not trivial even with complete data, we adopt Multiple imputation (MI) framework for verification bias …

Causal Inference In Longitudinal Studies With History-Restricted Marginal Structural Models, Romain Neugebauer, Mark J. Van Der Laan, Ira B. Tager

U.C. Berkeley Division of Biostatistics Working Paper Series

Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because MSM parameters provide explicit representations of causal effects. We introduce History-Restricted Marginal Structural Models (HRMSMs) for longitudinal data for the purpose of defining causal parameters which may often be better suited for Public Health research. This new class of MSMs allows investigators to analyze the causal effect of a treatment on an outcome based on a fixed, shorter and user-specified history of exposure compared to MSMs. By default, the latter represents the treatment causal effect of interest based on a treatment history defined by the …

The Sensitivity And Specificity Of Markers For Event Times, Tianxi Cai, Margaret S. Pepe, Thomas Lumley, Yingye Zheng, Nancy Swords Jenny

Harvard University Biostatistics Working Paper Series

No abstract provided.

New Confidence Intervals For The Difference Between Two Sensitivities At A Fixed Level Of Specificity, Gengsheng Qin, Yu-Sheng Hsu, Xiao-Hua Zhou

UW Biostatistics Working Paper Series

For two continuous-scale diagnostic tests, it is of interest to compare their sensitivities at a predetermined level of specificity. In this paper we propose three new intervals for the difference between two sensitivities at a fixed level of specificity. These intervals are easy to compute. We also conduct simulation studies to compare the relative performance of the new intervals with the existing normal approximation based interval proposed by Wieand et al (1989). Our simulation results show that the newly proposed intervals perform better than the existing normal approximation based interval in terms of coverage accuracy and interval length.

A Causal Inference Approach For Constructing Transcriptional Regulatory Networks, Biao Xing, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Transcriptional regulatory networks specify the interactions among regulatory genes and between regulatory genes and their target genes. Discovering transcriptional regulatory networks helps us to understand the underlying mechanism of complex cellular processes and responses. In this paper, we describe a causal inference approach for constructing transcriptional regulatory networks using gene expression data, promoter sequences and information on transcription factor binding sites. The method rst identies active transcription factors under each individual experiment using a feature selection approach similar to Bussemaker et al. (2001), Keles et al. (2002) and Conlon et al. (2003). Transcription factors are viewed as `treatments' and gene …

2d Quantitative Structure Activity Relationship Modeling Of Methylphenidate Analogues Using Algorithm And Partial Least Square Regression, Noureen Wadhwaniya

Theses

Quantitative Structure-Activity Relationship (QSAR) analysis attempts to develop a predictive model of biological activity based on molecular descriptors. 2D QSAR uses descriptors, such as topological indices, that are independent of molecular conformation. A genetic algorithm - partial least squares (GA-PLS) approach was used to identify the molecular descriptors that correlate to the biological activity (binding affinity) of a set of 80 methylphenidate analogues and to construct a predictive model. The GA code was implemented using the fitness function (1-(n-1)(1-q²)/ (n - c)), where n is the number of compounds, c is the optimal number of components, and q …

Singular Value Decomposition Of Analogs Of Gbr 12909, Anna Fiorentino

Theses

Analogs of GBR 12909 are drugs that could potentially be used to treat cocaine addiction. Singular Value Decomposition (SVD) is a multivariate analysis technique used to show relationships between the data and the variables associated with the data. The input data consists of the conformers of each analog (DM324, 728 conformers; TP250, 739 conformers) along with the eight torsional angles (Al, A2, B1-B6). A novel scaling technique was developed to address the problem of data circularity by subtracting the values of the torsional angles of the global energy minimum conformation from those of each conformer.

In SVD the original data …

Slimsvm : A Simple Implementation Of Support Vector Machine For Analysis Of Microarray Data, Avik Karmaker

Theses

Support Vector Machine (SVM) is a supervised machine learning technique being widely used in multiple areas of biological analysis including microarray data analysis. SlimSVM has been developed with the intention of replacing OSU SVM as the classification component of GenoIterSVM in order to make it independent of other SVM packages. GenolterSVM, developed by Dr. Marc Ma, is a SVM implementation with an iterative refinement algorithm for improved accuracy of classification of genotype microarray data. SlimSVM is an object-oriented, modular, and easy-to-use implementation written in C++. It supports dot (linear) and polynomial (non-linear) kernels. The program has been tested with artificial …

Analysis Of Auf1 Targeted Mrna Sequences, Jiebo Lu

Theses

AUF 1, an A+U rich element (ARE) binding protein, plays an important role in mRNA decay. To identify the mRNAs that interact with AUF 1, mRNA derived from a human cardiac cDNA expression library was purified by AUF 1 affinity chromatography and cloned following RT-PCR. 261 sequences were obtained. The sequences were searched against two protein databases and four nucleic acid databases, and the sequence information and database search results were input into a local Microsoft Access database, BLAST-AUF 1, by Java applets for parsing document. Analysis of protein information by querying BLAST-AUF 1 identified 194 function-known proteins, which were …

Analysis Of Molecular Conformations Using Relative Planes, Deepa S. Pai

Theses

Ring substructures of a drug usually participate actively in binding to the receptor. It is necessary to study the spatial relationship of these molecular recognition features in order to determine the pharmacophore of the drug. This is a particularly difficult problem when the drug is a flexible molecule with many energetically accessible conformations.

In this research an innovative approach to calculate the relative displacement and orientation of every possible pair of rings in a given molecule was designed, tested, and implemented in the "Planes" program. Planes were defined from each of the ring substructures and the displacement and rotation of …

Random Search Conformational Analysis Of Piperazine And Piperadine Analogs Of Gbr12909 : Implicit Aqueous Solvation Effects, William A. Roosma

Theses

The object of this work was to study the effect of solvent on the conformational potential energy surface (PES) of GBR12909 analogs. Local minima on the PES's were found by the Random Search algorithm using the Sybyl molecular modeling package from Tripos, Inc., and an implicit solvent model. Two force-field/charge models were employed in the analysis: the Tripos force field with Gasteiger-Huckel charges and the MMFF94 force field with MMFF94 charges.

The effect of solvent on the location of minima in multi-dimensional torsional angle space was studied by comparison to the vacuum phase results. Minima were plotted in torsional angle …

An Analysis Of The Periodicity Of The Cell Cycle And Apoptotic Regulatory Proteins In Prostate Xenografts Using Anova And Cosinor Methods, Aleen Hosdaghian

Theses

Circadian rhythms have been found in both plants and animals, in normal tissues as well as in most tumors and human cancers. By following these rhythms in healthy and cancerous tissue, it has been possible to find optimal times to deliver a dose of drug, such that efficacy is maximized and toxicity to normal tissues is minimized. In this study, the periodicity of several cell cycle and apoptotic regulatory proteins were studied in two prostate cancer models against a dietary therapeutic agent, Selenium. The ALVA-3 1 (androgen-independent) and PC-3 (androgen-independent) prostate cancer cell lines were grown in vivo, as a …

Ranked Set Sampling Based On Binary Water Quality Data With Covariates, Paul Kvam

Department of Math & Statistics Faculty Publications

A ranked set sample (RSS) is composed of independent order statistics, formed by collecting and ordering independent subsamples, then measuring only one item from each subsample. If the cost of sampling is dominated by data measurement rather than collection or ranking, the RSS technique is known to be superior to ordinary sampling. Experiments based on binary data are not designed to exploit the advantages of ranked set sampling because categorical data typically are as easily measured as ranked, making RSS methods impractical. However, in some environmental and biological studies, the success probability of a bivariate outcome is related to one …

Estimation Of Parameters In Replicated Time Series Regression Models, Genming Shi

Mathematics & Statistics Theses & Dissertations

The time series regression model was widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this thesis, we study the application of quasi-least squares, a relatively new method, to estimate the parameters in replicated time series models with general ARMA( p, q) correlation structure. We also study several established methods for estimating the parameters in those models, including the maximum likelihood, method of moments, and the GEE method. Asymptotic comparisons of the methods are made bV fixing the number of repeated measurements in each series, and …

Geographic Variation In The Morphology Of Crotalus Horridus (Serpentes: Viperidae), John Robert Allsteadt

Biological Sciences Theses & Dissertations

The Timber Rattlesnake (Crotalus horridus) occurs in discontinuous populations throughout the eastern and central United States. The species exhibits high levels of polymorphism in morphological traits, especially in coloration and pattern. Previous studies recognized either distinct northern and southern subspecies or three regional morphs (northern, southern, and western), but conflicting data sets and limited geographic sampling of previous studies have left the relationships among those regional variants unclear. In this study, univariate and multivariate statistics, together with a geographic information system, were used to analyze geographic variation in 36 morphological characters recorded from 2,420 specimens of C. horridus …

Cultural And Psychological Influences On Diabetic Adherence, Keikilani Mcmillin-Williams

Loma Linda University Electronic Theses, Dissertations & Projects

Diabetes mellitus is a serious disease that poses a particular healthcare challenge because progression is considered controllable (Cox, et al, 1985; Vinicor, et al, 1996) yet treatment adherence, and thus outcome, is very poor (Gonder-Frederick, Cox, & Ritterband, 2002; Goodall, 1991). Culture is a lethal risk factor for diabetic contraction and treatment maintenance. Latinos within the United States are two-to-three times more likely to develop complications and die than non-Latinos (Haffner et al, 1996; Rubin, Peyrot, & Saudek, 1991) and are less likely to adhere to treatment (Lipton, Losey, Giachello, Mendez, & Girotti, 1998). Efforts to eliminate health disparities have …

An Application In Bioinformatics : A Comparison Of Affymetrix And Compugen Human Genome Microarrays, Milind Misra

Theses

The human genome microarrays from Compugen® and Affymetrix® were compared in the context of the emerging field of computational biology. The two premier database servers for genomic sequence data, the National Center for Biotechnology Information and the European Bioinformatics Institute, were described in detail. The various databases and data mining tools available through these data servers were also discussed. Microarrays were examined from a historical perspective and their main current applications-expression analysis, mutation analysis, and comparative genomic hybridization-were discussed. The two main types of microarrays, cDNA spotted microarrays and high-density spotted microarrays were analyzed by exploring the human genome microarray …

A Method For Developing In-Silico Protein Homologs, Susan Mcclatchy

Theses

Computational methods for identifying and screening the most promising drug receptor candidates in the human genome are of great interest to drug discovery researchers. Successful methods will accurately identify and narrow the field of potential drug receptor candidates. This study details one such method.

The method described here begins with the assumption that novel drug receptors have high sequence similarity to established drug receptors. The similarity search program FASTA3 aligns translated sequences of the human genome to known drug receptor sequences and ranks these alignments by measuring their statistical significance. Query results returned by FASTA3 are assembled into "in-silico proteins" …

Analysis Of Gene Expression Data Using Expressionist 3.1 And Genespring 4.2, Indu Shrivastava

Theses

The purpose of this study was to determine the differences in the gene expression analysis methods of two data mining tools, Expressionistic^TM 3.1 and GeneSpring^TM 4.2 with focus on basic statistical analysis and clustering algorithms. The data for this analysis was derived from the hybridization of Rattus norvegicus RNA to the Affymetrix RG34A GeneChip. This analysis was derived from experiments designed to identify changes in gene expression patterns that were induced in vivo by an experimental treatment.

The tools were found to be comparable with respect to the list of statistically significant genes that were up-regulated by more …