Biostatistics Commons^™

Control Function Assisted Ipw Estimation With A Secondary Outcome In Case-Control Studies, Tamar Sofer, Marilyn C. Cornelis, Peter Kraft, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

Statistical Modeling And Prediction Of Hiv/Aids Prognosis: Bayesian Analyses Of Nonlinear Dynamic Mixtures, Xiaosun Lu

USF Tampa Graduate Theses and Dissertations

Statistical analyses and modeling have contributed greatly to our understanding of the pathogenesis of HIV-1 infection; they also provide guidance for the treatment of AIDS patients and evaluation of antiretroviral (ARV) therapies. Various statistical methods, nonlinear mixed-effects models in particular, have been applied to model the CD4 and viral load trajectories. A common assumption in these methods is all patients come from a homogeneous population following one mean trajectories. This assumption unfortunately obscures important characteristic difference between subgroups of patients whose response to treatment and whose disease trajectories are biologically different. It also may lack the robustness against population heterogeneity …

Association Between Class Iii Obesity (Bmi Of 40-59 Kg/M2) And Mortality: A Pooled Analysis Of 20 Prospective Studies, Cari M. Kitahara, Alan J. Flint, Amy Berrington De Gonzalez, Leslie Bernstein, Michelle Brotzman, Kim Robien, +30 Additional Authors

Epidemiology Faculty Publications

Background

The prevalence of class III obesity (body mass index [BMI]≥40 kg/m2) has increased dramatically in several countries and currently affects 6% of adults in the US, with uncertain impact on the risks of illness and death. Using data from a large pooled study, we evaluated the risk of death, overall and due to a wide range of causes, and years of life expectancy lost associated with class III obesity.

Methods and Findings

In a pooled analysis of 20 prospective studies from the United States, Sweden, and Australia, we estimated sex- and age-adjusted total and cause-specific mortality rates (deaths per …

Super-Learning Of An Optimal Dynamic Treatment Rule, Alexander R. Luedtke, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider the estimation of an optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric, beyond possible knowledge about the treatment and censoring mechanisms. We propose data adaptive estimators of this optimal dynamic regime which are defined by sequential loss-based learning under both the blip function and weighted classification frameworks. Rather than \textit{a priori} selecting …

Targeted Learning Of The Mean Outcome Under An Optimal Dynamic Treatment Rule, Mark J. Van Der Laan, Alexander R. Luedtke

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider estimation of and inference for the mean outcome under the optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric beyond possible knowledge about the treatment and censoring mechanism. This contrasts from the current literature that relies on parametric assumptions. We establish that the mean of the counterfactual outcome under the optimal dynamic treatment …

Predicting The Future Subject's Outcome Via An Optimal Stratification Procedure With Baseline Information, Florence H. Yong, Lu Tian, Sheng Yu, Tianxi Cai, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Tissue Triage And Freezing For Models Of Skeletal Muscle Disease, Hui Meng, Paul M. L. Janssen, Robert W. Grange, Lin Yang, Alan H. Beggs, Lindsay C. Swanson, Stacy A. Cossette, Alison Frase, Martin K. Childers, Henk Granzier, Emanuela Gussoni, Michael W. Lawlor

Biostatistics Faculty Publications

Skeletal muscle is a unique tissue because of its structure and function, which requires specific protocols for tissue collection to obtain optimal results from functional, cellular, molecular, and pathological evaluations. Due to the subtlety of some pathological abnormalities seen in congenital muscle disorders and the potential for fixation to interfere with the recognition of these features, pathological evaluation of frozen muscle is preferable to fixed muscle when evaluating skeletal muscle for congenital muscle disease. Additionally, the potential to produce severe freezing artifacts in muscle requires specific precautions when freezing skeletal muscle for histological examination that are not commonly used when …

Trends And Determinants Of Up-To-Date Status With Colorectal Cancer Screening In Tennessee, 2002-2008, Sreenivas P. Veeranki, Shimin Zheng

ETSU Faculty Works

BACKGROUND:

Screening rates for colorectal cancer (CRC) are increasing nationwide including Tennessee (TN); however, their up-to-date status is unknown. The objective of this study is to determine the trends and characteristics of TN adults who are up-to-date status with CRC screening during 2002-2008.

METHODS:

We examined data from the TN Behavioral Risk Factor Surveillance System for 2002, 2004, 2006 and 2008 to estimate the proportion of respondents aged 50 years and above who were up-to-date status with CRC screening, defined as an annual home fecal occult blood test and/or sigmoidoscopy or colonoscopy in the past 5 years. We identified trends …

Rationale, Design, And Baseline Characteristics Of A Randomized, Placebo-Controlled Cardiovascular Outcome Trial Of Empagliflozin (Empa-Reg Outcometm), Bernard Zinman, Silvio E. Inzucchi, John M. Lachin, Christoph Wanner, Roberto Ferrari, David Fitchett, Erich Bluhmki, Stefan Hantel, Joan Kempthorne-Rawson, Jennifer Newman, Odd Erik Johansen, Hans Juergen Woerle, Uli C. Broedl

Epidemiology Faculty Publications

Background

Evidence concerning the importance of glucose lowering in the prevention of cardiovascular (CV) outcomes remains controversial. Given the multi-faceted pathogenesis of atherosclerosis in diabetes, it is likely that any intervention to mitigate this risk must address CV risk factors beyond glycemia alone. The SGLT-2 inhibitor empagliflozin improves glucose control, body weight and blood pressure when used as monotherapy or add-on to other antihyperglycemic agents in patients with type 2 diabetes. The aim of the ongoing EMPA-REG OUTCOMETM trial is to determine the long-term CV safety of empagliflozin, as well as investigating potential benefits on microvascular outcomes.

Methods

Patients who …

Genetic Analysis Workshop 18: Methods And Strategies For Analyzing Human Sequence And Phenotype Data In Members Of Extended Pedigrees, Heike Bickeböller, Julia N. Bailey, Joseph Beyene, Rita M. Cantor, Heather J. Cordell, Robert C. Culverhouse, Corinne D. Engelman, David W. Fardo, Saurabh Ghosh, Inke R. König, Justo Lorenzo Bermejo, Phillip E. Melton, Stephanie A. Santorico, Glen A. Satten, Lei Sun, Nathan L. Tintle, Andreas Ziegler, Jean W. Maccluer, Laura Almasy

Biostatistics Faculty Publications

Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted …

On Family-Based Genome-Wide Association Studies With Large Pedigrees: Observations And Recommendations, David W. Fardo, Xue Zhang, Lili Ding, Hua He, Brad Kurowski, Eileen S. Alexander, Tesfaye B. Mersha, Valentina Pilipenko, Leah Kottyan, Kannabiran Nandakumar, Lisa Martin

Biostatistics Faculty Publications

Family based association studies are employed less often than case-control designs in the search for disease-predisposing genes. The optimal statistical genetic approach for complex pedigrees is unclear when evaluating both common and rare variants. We examined the empirical power and type I error rates of 2 common approaches, the measured genotype approach and family-based association testing, through simulations from a set of multigenerational pedigrees. Overall, these results suggest that much larger sample sizes will be required for family-based studies and that power was better using MGA compared to FBAT. Taking into account computational time and potential bias, a 2-step strategy …

Using Mendelian Inheritance Errors As Quality Control Criteria In Whole Genome Sequencing Data Set, Valentina V. Pilipenko, Hua He, Brad G. Kurowski, Eileen S. Alexander, Xue Zhang, Lili Ding, Tesfaye B. Mersha, Leah Kottyan, David W. Fardo, Lisa J. Martin

Biostatistics Faculty Publications

Although the technical and analytic complexity of whole genome sequencing is generally appreciated, best practices for data cleaning and quality control have not been defined. Family based data can be used to guide the standardization of specific quality control metrics in nonfamily based data. Given the low mutation rate, Mendelian inheritance errors are likely as a result of erroneous genotype calls. Thus, our goal was to identify the characteristics that determine Mendelian inheritance errors. To accomplish this, we used chromosome 3 whole genome sequencing family based data from the Genetic Analysis Workshop 18. Mendelian inheritance errors were provided as part …

A 2-Step Penalized Regression Method For Family-Based Next-Generation Sequencing Association Studies, Xiuhua Ding, Shaoyong Su, Kannabiran Nandakumar, Xiaoling Wang, David W. Fardo

Biostatistics Faculty Publications

Large-scale genetic studies are often composed of related participants, and utilizing familial relationships can be cumbersome and computationally challenging. We present an approach to efficiently handle sequencing data from complex pedigrees that incorporates information from rare variants as well as common variants. Our method employs a 2-step procedure that sequentially regresses out correlation from familial relatedness and then uses the resulting phenotypic residuals in a penalized regression framework to test for associations with variants within genetic units. The operating characteristics of this approach are detailed using simulation data based on a large, multigenerational cohort.

Modeling Of Multivariate Longitudinal Phenotypes In Family Genetic Studies With Bayesian Multiplicity Adjustment, Lili Ding, Brad G. Kurowski, Hua He, Eileen S. Alexander, Tesfaye B. Mersha, David Fardo, Xue Zhang, Valentina V. Pilipenko, Leah Kottyan, Lisa J. Martin

Biostatistics Faculty Publications

Genetic studies often collect data on multiple traits. Most genetic association analyses, however, consider traits separately and ignore potential correlation among traits, partially because of difficulties in statistical modeling of multivariate outcomes. When multiple traits are measured in a pedigree longitudinally, additional challenges arise because in addition to correlation between traits, a trait is often correlated with its own measures over time and with measurements of other family members. We developed a Bayesian model for analysis of bivariate quantitative traits measured longitudinally in family genetic studies. For a given trait, family-specific and subject-specific random effects account for correlation among family …

Interadapt -- An Interactive Tool For Designing And Evaluating Randomized Trials With Adaptive Enrollment Criteria, Aaron Joel Fisher, Harris Jaffee, Michael Rosenblum

Johns Hopkins University, Dept. of Biostatistics Working Papers

The interAdapt R package is designed to be used by statisticians and clinical investigators to plan randomized trials. It can be used to determine if certain adaptive designs offer tangible benefits compared to standard designs, in the context of investigators’ specific trial goals and constraints. Specifically, interAdapt compares the performance of trial designs with adaptive enrollment criteria versus standard (non-adaptive) group sequential trial designs. Performance is compared in terms of power, expected trial duration, and expected sample size. Users can either work directly in the R console, or with a user-friendly shiny application that requires no programming experience. Several added …

Vitamin D Status And Demographic And Lifestyle Determinants Among Adults In The United States (Nhanes 2001-2006), Yan Cao, Katie L. Callahan, Sreenivas P. Veeranki, Yang Chen, Ying Liu, Shimin Zheng

ETSU Faculty Works

This study looked at risk factors associated with vitamin D levels in the body among a representative sample of adults in the U.S., NHANES III (2001-2006) data were used to assess the relationship between several demographic and health risk factors and vitamin D levels in the body. The Baseline-Category Logit Model was used to test the association between vitamin D level and the potential risk factors age, education, ethnicity, poverty status, physical activity, smoking, alcohol, obesity, diabetes and total cholesterol with both genders. Vitamin D insufficiency and deficiency were significantly associated with age, race, education, physical activity, obesity, diabetes and …

Methods For Exploring Treatment Effect Heterogeneity In Subgroup Analysis: An Application To Global Clinical Trials, I. Manjula Schou, Ian C. Marschner

COBRA Preprint Series

Multi-country randomised clinical trials (MRCTs) are common in the medical literature and their interpretation has been the subject of extensive recent discussion. In many MRCTs, an evaluation of treatment effect homogeneity across countries or regions is conducted. Subgroup analysis principles require a significant test of interaction in order to claim heterogeneity of treatment effect across subgroups, such as countries in a MRCT. As clinical trials are typically underpowered for tests of interaction, overly optimistic expectations of treatment effect homogeneity can lead researchers, regulators and other stakeholders to over-interpret apparent differences between subgroups even when heterogeneity tests are insignificant. In this …

Pgs: A Tool For Association Study Of High-Dimensional Microrna Expression Data With Repeated Measures, Yinan Zheng, Zhe Fei, Wei Zhang, Justin Starren, Lei Liu, Andrea Baccarelli, Yi Li, Lifang Hou

The University of Michigan Department of Biostatistics Working Paper Series

Motivation: MicroRNAs (miRNAs) are short single-stranded non-coding molecules that usually function as negative regulators to silence or suppress gene expression. Due to interested in the dynamic nature of the miRNA and reduced microarray and sequencing costs, a growing number of researchers are now measuring high-dimensional miRNAs expression data using repeated or multiple measures in which each individual has more than one sample collected and measured over time. However, the commonly used site-by-site multiple testing may impair the value of repeated or multiple measures data by ignoring the inherent dependent structure, which lead to problems including underpowered results after multiple comparison …

Targeted Maximum Likelihood Estimation Using Exponential Families, Iván Díaz, Michael Rosenblum

Johns Hopkins University, Dept. of Biostatistics Working Papers

Targeted maximum likelihood estimation (TMLE) is a general method for estimating parameters in semiparametric and nonparametric models. Each iteration of TMLE involves fitting a parametric submodel that targets the parameter of interest. We investigate the use of exponential families to define the parametric submodel. This implementation of TMLE gives a general approach for estimating any smooth parameter in the nonparametric model. A computational advantage of this approach is that each iteration of TMLE involves estimation of a parameter in an exponential family, which is a convex optimization problem for which software implementing reliable and computationally efficient methods exists. We illustrate …

Gwas Identifies An Nat2 Acetylator Status Tag Single Nucleotide Polymorphism To Be A Major Locus For Skin Fluorescence, Karen M. Eny, Helen L. Lutgers, John Maynard, Barbara E.K. Klein, Kristine E. Lee, Patricia A. Cleary, +20 Additional Authors

Epidemiology Faculty Publications

Aims/hypothesis

Skin fluorescence (SF) is a non-invasive marker of AGEs and is associated with the long-term complications of diabetes. SF increases with age and is also greater among individuals with diabetes. A familial correlation of SF suggests that genetics may play a role. We therefore performed parallel genome-wide association studies of SF in two cohorts.

Methods

Cohort 1 included 1,082 participants, 35–67 years of age with type 1 diabetes. Cohort 2 included 8,721 participants without diabetes, aged 18–90 years.

Results

rs1495741 was significantly associated with SF in Cohort 1 (p < 6 × 10−10), which is known to tag theNAT2 acetylator phenotype. The fast acetylator genotype was associated …

Abcc9 Gene Polymorphism Is Associated With Hippocampal Sclerosis Of Aging Pathology, Peter T. Nelson, Steven Estus, Erin L. Abner, Ishita Parikh, Manasi Malik, Janna H. Neltner, Eseosa Ighodaro, Wang-Xia Wang, Bernard R. Wilfred, Li-San Wang, Walter A. Kukull, Kannabiran Nandakumar, Mark L. Farman, Wayne W. Poon, Maria M. Corrada, Claudia H. Kawas, David H. Cribbs, David A. Bennett, Julie A. Schneider, Eric B. Larson, Paul K. Crane, Otto Valladares, Frederick A. Schmitt, Richard J. Kryscio, Gregory A. Jicha, Charles D. Smith, Stephen W. Scheff, Joshua A. Sonnen, Jonathan L. Haines, Margaret A. Pericak-Vance, Richard Mayeux, Lindsay A. Farrer, Linda J. Van Eldik, Craig Horbinski, Robert C. Green, Marla Gearing, Leonard W. Poon, Patricia L. Kramer, Randall L. Woltjer, Thomas J. Montine, Amanda B. Partch, Alexander J. Rajic, Katierose Richmire, Sarah E. Monsell, Gerard D. Schellenberg, David W. Fardo

Pathology and Laboratory Medicine Faculty Publications

Hippocampal sclerosis of aging (HS-Aging) is a high-morbidity brain disease in the elderly but risk factors are largely unknown. We report the first genome-wide association study (GWAS) with HS-Aging pathology as an endophenotype. In collaboration with the Alzheimer's Disease Genetics Consortium, data were analyzed from large autopsy cohorts: (#1) National Alzheimer's Coordinating Center (NACC); (#2) Rush University Religious Orders Study and Memory and Aging Project; (#3) Group Health Research Institute Adult Changes in Thought study; (#4) University of California at Irvine 90+ Study; and (#5) University of Kentucky Alzheimer's Disease Center. Altogether, 363 HS-Aging cases and 2,303 controls, all pathologically …

Partially-Latent Class Models (Plcm) For Case-Control Studies Of Childhood Pneumonia Etiology, Zhenke Wu, Maria Deloria-Knoll, Laura L. Hammitt, Scott L. Zeger

Johns Hopkins University, Dept. of Biostatistics Working Papers

In population studies on the etiology of disease, one goal is the estimation of the fraction of cases attributable to each of several causes. For example, pneumonia is a clinical diagnosis of lung infection that may be caused by viral, bacterial, fungal, or other pathogens. The study of pneumonia etiology is challenging because directly sampling from the lung to identify the etiologic pathogen is not standard clinical practice in most settings. Instead, measurements from multiple peripheral specimens are made. This paper considers the problem of estimating the population etiology distribution and the individual etiology probabilities. We formulate the scientific …

Deductive Derivation And Computerization Of Compatible Semiparametric Efficient Estimation, Constantine E. Frangakis, Tianchen Qian, Zhenke Wu, Ivan Diaz

U.C. Berkeley Division of Biostatistics Working Paper Series

Researchers often seek robust inference for a parameter through semiparametric estimation. Efficient semiparametric estimation currently requires theoretical derivation of the efficient influence function (EIF), which can be a challenging and time-consuming task. If this task can be computerized, it can save dramatic human effort, which can be transferred, for example, to the design of new studies. Although the EIF is, in principle, a derivative, simple numerical differentiation to calculate the EIF by a computer masks the EIF's functional dependence on the parameter of interest. For this reason, the standard approach to obtaining the EIF has been the theoretical construction of …

Dose Expansion Cohorts In Phase I Trials, Alexia Iasonos, John O'Quigley

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

A rapidly increasing number of Phase I dose-finding studies, and in particular those based on the standard 3+3 design, frequently prolong the study and include dose expansion cohorts (DEC) with the goal to better characterize the toxicity profiles of experimental agents and to study disease specific cohorts. These trials consist of two phases: the usual dose escalation phase that aims to establish the maximum tolerated dose (MTD) and the dose expansion phase that accrues additional patients, often with different eligibility criteria, and where additional information is being collected. Current protocols typically do not specify whether the MTD will be updated …

Variable Selection For Zero-Inflated And Overdispersed Data With Application To Health Care Demand In Germany, Zhu Wang, Shuangge Ma, Ching-Yun Wang

COBRA Preprint Series

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized …

Targeted Covariate-Adjusted Response-Adaptive Lasso-Based Randomized Controlled Trials, Antoine Chambaz, Wenjing Zheng, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We present a new covariate-adjusted response-adaptive randomized controlled trial design and inferential procedure built on top of it. The procedure is targeted in the sense that (i) the sequence of randomization schemes is group-sequentially determined by targeting a user-specified optimal randomization design based on accruing data and, (ii) our estimator of the user-specified parameter of interest, seen as the value of a functional evaluated at the true, unknown distribution of the data, is targeted toward it by following the paradigm of targeted minimum loss estimation. We focus for clarity on the case that the parameter of interest is the marginal …

Variable-Domain Functional Regression For Modeling Icu Data, Jonathan E. Gellar, Elizabeth Colantuoni, Dale M. Needham, Ciprian M. Crainiceanu

Johns Hopkins University, Dept. of Biostatistics Working Papers

We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registration. Methods were motivated by and applied to a study of association between daily measures of the Intensive Care Unit (ICU) Sequential Organ Failure Assessment (SOFA) score and two outcomes: in-hospital mortality, and physical impairment …

Sieve Plateau Variance Estimators: A New Approach To Confidence Interval Estimation For Dependent Data, Molly M. Davies, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Suppose we have a data set of n-observations where the extent of dependence between them is poorly understood. We assume we have an estimator that is squareroot-consistent for a particular estimand, and the dependence structure is weak enough so that the standardized estimator is asymptotically normally distributed. Our goal is to estimate the asymptotic variance of the standardized estimator so that we can construct a Wald-type confidence interval for the estimate. In this paper we present an approach that allows us to learn this asymptotic variance from a sequence of influence function based candidate variance estimators. We focus on time …

Patient Rule Induction Method For Subgroup Identification Given Censored Data., Patrick James Trainor

Electronic Theses and Dissertations

The identification of subgroups in clinical studies is an important aspect of personalized medicine. In order to develop tailored therapeutics, the factors that characterize subgroups with differential prognosis, response to treatment, and incidence of adverse events or toxicities must be elucidated. We present a generalization of a statistical learning algorithm, Patient Rule Induction Method (PRIM), that is well suited for this task given a right-censored time-to-event outcome measure. This algorithm works to recursively partition a covariate space into mutually exclusive boxes that can be utilized to define subgroups. Conceptually the algorithm is similar to classification and regression trees but rather …

Statistical Methods For Assessing Treatment Effects For Observational Studies., Kristopher C. Gardner 1984-

Electronic Theses and Dissertations

Though randomized clinical (RCTs) trials are the gold standard for comparing treatments, they are often infeasible or exclude clinically important subjects, or generally represent an idealized medical setting rather than real practice. Observational data provide an opportunity to study practice-based evidence, but also present challenges for analysis. Traditional statistical methods which are suitable for RCTs may be inadequate for the observational studies. In this project, four of the most popular statistical methods for observational studies: ANCOVA, propensity score matching, regression with the propensity score as a covariate, and instrumental variables (IV) are investigated through application to MarketScan insurance claims data. …