Biostatistics Commons^™

Compound Identification Using Penalized Linear Regression., Ruiqi Liu

Electronic Theses and Dissertations

In this study, we propose a new method for compound identification using penalized linear regression. Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. In the context of the linear regression, the response variable is an experimental mass spectrum (i.e., query) and all the compounds in the reference library are the independent variables. However, the number of compounds in the reference library is much larger than the range of m/z values so that the data become high dimensional data with suffering from singularity. For …

Development Of Novel Methods To Minimize The Impact Of Sequencing Errors In The Next-Generation Sequencing Data Analysis, Xiaofeng Zheng

Dissertations and Theses (Open Access)

Next-generation sequencing (NGS) technology has become a prominent tool in biological and biomedical research. However, NGS data analysis, such as de novo assembly, mapping and variants detection is far from maturity, and the high sequencing error-rate is one of the major problems. .

To minimize the impact of sequencing errors, we developed a highly robust and efficient method, MTM, to correct the errors in NGS reads. We demonstrated the effectiveness of MTM on both single-cell data with highly non-uniform coverage and normal data with uniformly high coverage, reflecting that MTM’s performance does not rely on the coverage of the sequencing …

Integrative Biomarker Identification And Classification Using High Throughput Assays, Pan Tong

Dissertations and Theses (Open Access)

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays …

Structured Functional Principal Component Analysis, Haochang Shou, Vadim Zipunnikov, Ciprian Crainiceanu, Sonja Greven

Johns Hopkins University, Dept. of Biostatistics Working Papers

Motivated by modern observational studies, we introduce a class of functional models that expands nested and crossed designs. These models account for the natural inheritance of correlation structure from sampling design in studies where the fundamental sampling unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for ultra-high dimensional data. Methods are illustrated in three examples: high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity …

Determinan Komplikasi Kronik Diabetes Melitus Pada Lanjut Usia, Amrina Rosyada, Indang Trihandini

Kesmas

Indonesia menghadapi jumlah penduduk lanjut usia (lansia) yang semakin meningkat dan diikuti oleh peningkatan frekuensi penyakit tidak menular kronis atau multimorbiditas. Penelitian ini bertujuan untuk mengetahui prevalensi dan faktor yang berhubungan komplikasi kronis pada lansia penderita diabetes melitus. Penelitian ini menggunakan data Riset Kesehatan Dasar (Riskesdas) Tahun 2007 dengan desain cross sectional representatif Indonesia dan metode cluster 2 tahap untuk pengambilan sampel. Sampel adalah 1.565 lansia penderita diabetes melitus. Metode analisis yang digunakan meliputi analisis deskriptif dan multivariat. Hasil analisis menunjukkan bahwa prevalensi komplikasi kronis pada lansia adalah sekitar 73,1%, dengan hipertensi sebagai komplikasi terbanyak. Berdasarkan analisis multivariat diketahui pula …

Choosing The Cut Point For A Restricted Mean In Survival Analysis, A Data Driven Method, Emily H. Sheldon

Theses and Dissertations

Survival Analysis generally uses the median survival time as a common summary statistic. While the median possesses the desirable characteristic of being unbiased, there are times when it is not the best statistic to describe the data at hand. Royston and Parmar (2011) provide an argument that the restricted mean survival time should be the summary statistic used when the proportional hazards assumption is in doubt. Work in Restricted Means dates back to 1949 when J.O. Irwin developed a calculation for the standard error of the restricted mean using Greenwood’s formula. Since then the development of the restricted mean has …

Detecting And Correcting Batch Effects In High-Throughput Genomic Experiments, Sarah Reese

Theses and Dissertations

Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal components analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data. We present an extension of principal components analysis to quantify the existence of batch effects, called guided PCA (gPCA). We describe a …

Penalized Function-On-Function Regression, Andrada E. Ivanescu, Ana-Maria Staicu, Fabian Scheipl, Sonja Greven

Johns Hopkins University, Dept. of Biostatistics Working Papers

We propose a general framework for smooth regression of a functional response on one or multiple functional predictors. Using the mixed model representation of penalized regression expands the scope of function on function regression to many realistic scenarios. In particular, the approach can accommodate a densely or sparsely sampled functional response as well as multiple functional predictors that are observed: 1) on the same or different domains than the functional response; 2) on a dense or sparse grid; and 3) with or without noise. It also allows for seamless integration of continuous or categorical covariates and provides approximate confidence intervals …

Daily Walking And Life Expectancy Of Elderly People In The Iowa 65+ Rural Health Study, Hani M. Samawi

Biostatistics: Faculty Publications

The purpose of this paper is to investigate the hypothesis that outdoor daily walking, as an exercise, has an effect on the rate of mortality among those elderly people in the Iowa 65+ Rural Health Study (RHS). RHS is a prospective longitudinal cohort study of 8 years follow-up from 1981 to 1989. It consists of a random sample of 3,673 individuals (1,420 men and 2,253 women) aged 65 or older living in Washington and Iowa counties of the State of Iowa. Our analysis was conducted only on those non-institutional individuals who could without any help walk across a small room; …

Characterization Of A Weighted Quantile Score Approach For Highly Correlated Data In Risk Analysis Scenarios, Caroline Carrico

Theses and Dissertations

In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) are unsuitable. We extend and characterize a weighted quantile score (WQS) approach to estimating an index for a set of highly correlated components. In the case with environmental chemicals, we use the WQS to identify “bad actors” and estimate body burden. The accuracy of the WQS was evaluated through extensive simulation studies in terms of validity (ability of the WQS …

Vertically Shifted Mixture Models For Clustering Longitudinal Data By Shape, Brianna C. Heggeseth, Nicholas P. Jewell

U.C. Berkeley Division of Biostatistics Working Paper Series

Longitudinal studies play a prominent role in health, social and behavioral sciences as well as in the biological sciences, economics, and marketing. By following subjects over time, temporal changes in an outcome of interest can be directly observed and studied. An important question concerns the existence of distinct trajectory patterns. One way to determine these distinct patterns is through cluster analysis, which seeks to separate objects (subjects, patients, observational units) into homogeneous groups. Many methods have been adapted for longitudinal data, but almost all of them fail to explicitly group trajectories according to distinct pattern shapes. To fulfill the need …

Efficient Estimation Of Risk Ratios From Clustered Binary Data, Matthew Cefalu, Eric Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

Predicting Human Movement Type Based On Multiple Accelerometers Using Movelets, Bing He, Jiawei Bai, Annemarie Koster, Casserotti Paolo, Nancy Glynn, Tamara B. Harris, Ciprian Crainiceanu

Johns Hopkins University, Dept. of Biostatistics Working Papers

We introduce statistical methods for prediction of types of human movement based on three tri-axial accelerometers worn simultaneously at the hip, left, and right wrist. We compare the individual performance of the three accelerometers using movelets and propose a new prediction algorithm that integrates the information from all three accelerometers. The development is motivated by a study of 20 older subjects who were instructed to perform 15 different types of activities during in-laboratory sessions. The differences in the prediction performance for different activity types among the three accelerometers reveal subtle yet important insights into how the intrinsic physical features of …

Is Obesity Socially Contagious?, Ciani Jean Sparks

Statistics

The main objective of this paper is to analyze three different articles that discuss whether obesity could be socially contagious. According to the World Health Organization in 2013, obesity is the fifth leading risk for deaths around the world. This disease has dramatically increased in the last decade, which has led scientists to believe there are other factors contributing to the epidemic besides genetics. The first article I analyzed, written by Nicholas Christakis and James Fowler, provided a logistic regression model to estimate the odds of a person becoming obese. The model included the explanatory variables: age, sex, education, smoking …

A Bayesian Regression Tree Approach To Identify The Effect Of Nanoparticles Properties On Toxicity Profiles, Cecile Low-Kam, Haiyuan Zhang, Zhaoxia Ji, Tian Xia, Jeffrey I. Zinc, Andre Nel, Donatello Telesca

COBRA Preprint Series

We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose and time-response surfaces smoothing. The resulting posterior distribution is sampled via a Markov Chain Monte Carlo algorithm. This …

Asymptotic And Finite Sample Behavior Of Net Reclassification Indices, Zheyu Wang

UW Biostatistics Working Paper Series

The Net Reclassification Index (NRI) introduced by Pencina and colleagues [1, 2] is designed to quantify the prediction increment provided by a new biomarker. It has become popular for evaluating and selecting novel markers. The published variance formulae for NRI statistics do not account for the fact that risks are estimated based on risk models fit to data, and thus are not valid in practice when estimated risks are used [3]. Kerr and colleagues [4] showed that the confidence intervals constructed based on a bootstrap estimate of the variance and Normal approximation had the best performance among various methods they …

On The Restricted Mean Event Time In Survival Analysis, Lu Tian, Lihui Zhao, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Surrogacy Assessment Using Principal Stratification When Surrogate And Outcome Measures Are Multivariate Normal, Anna Conlon, Jeremy M.G. Taylor, Michael R. Elliott

The University of Michigan Department of Biostatistics Working Paper Series

No abstract provided.

Lifetime Musical Activities And Cognitive Function Of The Elderly, Alicia Nevriana, Pandu Riono, Tri Budi W. Rahardjo, Adji Kusumadjati

Kesmas

Penurunan fungsi kognitif merupakan salah satu masalah umum pada lanjut usia yang mampu memengaruhi kualitas hidup mereka. Musik merupakan sebuah elemen yang dipercaya mampu berkontribusi terhadap kualitas hidup mereka. Meski demikian, hubungan antara aktivitas musikal yang dilakukan sepanjang hidup dan fungsi kognitif lansia belum diketahui secara pasti. Pada penelitian ini, hubungan antara aktivitas musikal sepanjang hidup dan fungsi kognitif dievaluasi. Lima puluh tiga lansia penghuni panti tresna werdha di Jakarta Timur dipilih dan diwawancarai terkait karakteristik dan aktivitas musikal sepanjang hidup mereka. Fungsi kognitif juga diukur menggunakan MMSE. Hasil penelitian ini menunjukkan adanya kemungkinan asosiasi antara aktivitas musikal sepanjang hidup …

Targeted Estimation Of Variable Importance Measures With Interval-Censored Outcomes, Stephanie Sapp, Mark J. Van Der Laan, Kimberly Page

U.C. Berkeley Division of Biostatistics Working Paper Series

In most experimental and observational studies, participants are not followed in continuous time. Instead, data is collected about participants only at certain monitoring times. These monitoring times are random, and often participant specific. As a result, outcomes are only known up to random time intervals, resulting in interval-censored data. In contrast, when estimating variable importance measures on interval-censored outcomes, practitioners often ignore the presence of interval-censoring, and instead treat the data as continuous or right-censored, applying ad-hoc approaches to mask the true interval-censoring. In this paper, we describe Targeted Minimum Loss-based Estimation methods tailored for estimation of variable importance measures …

Progress Realized: Trends In Hiv-1 Viral Load And Cd4 Cell Count In A Tertiary-Care Center From 1999 Through 2011, Howard B. Gale, Manuel D. Rodriguez, Heather J. Hoffman, Debra A. Benator, Fred M. Gordin, Ann M. Labriola, Virginia L. Kan

Epidemiology Faculty Publications

Abstract

Accumulating evidence suggests that alterations in immune function may be important in the etiology of papillary thyroid cancer (PTC). To identify genetic markers in immune-related pathways, we evaluated 3,985 tag single nucleotide polymorphisms (SNPs) in 230 candidate gene regions (adhesion-extravasation-migration, arachidonic acid metabolism/eicosanoid signaling, complement and coagulation cascade, cytokine signaling, innate pathogen detection and antimicrobials, leukocyte signaling, TNF/NF-kB pathway or other) in a case-control study of 344 PTC cases and 452 controls. We used logistic regression models to estimate odds ratios (OR) and calculate one degree of freedom P values of linear trend (PSNP-trend) for the association …

Missing At Random And Ignorability For Inferences About Subsets Of Parameters With Missing Data, Roderick J. Little, Sahar Zanganeh

The University of Michigan Department of Biostatistics Working Paper Series

For likelihood-based inferences from data with missing values, Rubin (1976) showed that the missing data mechanism can be ignored when (a) the missing data are missing at random (MAR), in the sense that missingness does not depend on the missing values after conditioning on the observed data, and (b) the parameters of the data model and the missing-data mechanism are distinct; that is, there are no a priori ties, via parameter space restrictions or prior distributions, between the parameters of the data model and the parameters of the model for the mechanism. Rubin described (a) and (b) as the "weakest …

Accounting For Model Uncertainty In Linear Mixed-Effects Models, Adam Sima

Theses and Dissertations

Standard statistical decision-making tools, such as inference, confidence intervals and forecasting, are contingent on the assumption that the statistical model used in the analysis is the true model. In linear mixed-effect models, ignoring model uncertainty results in an underestimation of the residual variance, contributing to hypothesis tests that demonstrate larger than nominal Type-I errors and confidence intervals with smaller than nominal coverage probabilities. A novel utilization of the generalized degrees of freedom developed by Zhang et al. (2012) is used to adjust the estimate of the residual variance for model uncertainty. Additionally, the general global linear approximation is extended to …

Targeted Data Adaptive Estimation Of The Causal Dose Response Curve, Iván Díaz, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Estimation of the causal dose-response curve is an old problem in statistics. In a non parametric model, if the treatment is continuous, the dose-response curve is not a pathwise differentiable parameter, and no root-n-consistent estimator is available. However, the risk of a candidate algorithm for estimation of the dose response curve is a pathwise differentiable parameter, whose consistent and efficient estimation is possible. In this work, we review the cross validated augmented inverse probability of treatment weighted estimator (CV A-IPTW) of the risk, and present a cross validated targeted minimum loss based estimator (CV-TMLE) counterpart. These estimators are proven consistent …

Can Changes In Angiogenic Biomarkers Between The First And Second Trimesters Of Pregnancy Predict Development Of Pre-Eclampsia In A Low-Risk Nulliparous Patient Population?, Leslie Myatt, Rebecca G. Clifton, J. M. Roberts, Catherine Y. Spong, Ronald J. Wapner, +10 Additional Authors

GW Biostatistics Center

OBJECTIVE:

To determine if change in maternal angiogenic biomarkers between the first and second trimesters predicts pre-eclampsia in low-risk nulliparous women.

DESIGN:

A nested case-control study of change in maternal plasma soluble Flt-1 (sFlt-1), soluble endoglin (sEng) and placenta growth factor (PlGF). We studied 158 pregnancies complicated by pre-eclampsia and 468 normotensive nonproteinuric controls.

SETTING:

A multicentre study in 16 academic medical centres in the USA.

POPULATION:

Low-risk nulliparous women.

METHODS:

Luminex assays for PlGF, sFlt-1 and sEng performed on maternal EDTA plasma collected at 9-12, 15-18 and 23-26 weeks of gestation. Rate of change of analyte between first and …

Statistical Methods For Evaluating And Comparing Biomarkers For Patient Treatment Selection, Holly Janes, Marshall D. Brown, Margaret Pepe, Ying Huang

UW Biostatistics Working Paper Series

Despite the heightened interest in developing biomarkers predicting treatment response that are used to optimize patient treatment decisions, there has been relatively little development of statistical methodology to evaluate these markers. There is currently no unified statistical framework for marker evaluation. This paper proposes a suite of descriptive and inferential methods designed to evaluate individual markers and to compare candidate markers. An R software package has been developed which implements these methods. Their utility is illustrated in the breast cancer treatment context, where candidate markers are evaluated for their ability to identify a subset of women who do not benefit …

A Neural Network Model To Translate Brain Developmental Events Across Mammalian Species, Radhakrishnan Nagarajan, Jeffrey N. Jonkman

Biostatistics Faculty Publications

Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially useful since the distribution of the event timing data is skewed with a majority of events documented only across a few selected species. The present study investigates the choice of single hidden layer feed-forward neural networks (FFNN) for predicting the unknown events from the empirical data. A …

A General Regression Framework For A Secondary Outcome In Case-Control Studies, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

In Praise Of Simplicity Not Mathematistry! Ten Simple Powerful Ideas For The Statistical Scientist, Roderick J. Little

The University of Michigan Department of Biostatistics Working Paper Series

Ronald Fisher was by all accounts a first-rate mathematician, but he saw himself as a scientist, not a mathematician, and he railed against what George Box called (in his Fisher lecture) "mathematistry". Mathematics is the indispensable foundation for statistics, but our subject is constantly under assault by people who want to turn statistics into a branch of mathematics, making the subject as impenetrable to non-mathematicians as possible. Valuing simplicity, I describe ten simple and powerful ideas that have influenced my thinking about statistics, in my areas of research interest: missing data, causal inference, survey sampling, and statistical modeling in general. …

Modeling The Impact And Costs Of Semiannual Mass Drug Administration For Accelerated Elimination Of Lymphatic Filariasis, Wilma A. Stolk, Quirine A. Ten Bosch, Sake J. De Vlas, Peter U. Fischer, Gary J. Weil, Ann S. Goldman

Epidemiology Faculty Publications

The Global Program to Eliminate Lymphatic Filariasis (LF) has a target date of 2020. This program is progressing well in many countries. However, progress has been slow in some countries, and others have not yet started their mass drug administration (MDA) programs. Acceleration is needed. We studied how increasing MDA frequency from once to twice per year would affect program duration and costs by using computer simulation modeling and cost projections. We used the LYMFASIM simulation model to estimate how many annual or semiannual MDA rounds would be required to eliminate LF for Indian and West African scenarios with varied …