Statistical Methodology Commons^™

Mechanistic Mathematical Models: An Underused Platform For Hpv Research, Marc Ryser, Patti Gravitt, Evan R. Myers

Global Health Faculty Publications

Health economic modeling has become an invaluable methodology for the design and evaluation of clinical and public health interventions against the human papillomavirus (HPV) and associated diseases. At the same time, relatively little attention has been paid to a different yet complementary class of models, namely that of mechanistic mathematical models. The primary focus of mechanistic mathematical models is to better understand the intricate biologic mechanisms and dynamics of disease. Inspired by a long and successful history of mechanistic modeling in other biomedical fields, we highlight several areas of HPV research where mechanistic models have the potential to advance the …

Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen

U.C. Berkeley Division of Biostatistics Working Paper Series

WHO guidelines call for universal antiretroviral treatment, and UNAIDS has set a global target to virally suppress most HIV-positive individuals. Accurate estimates of population-level coverage at each step of the HIV care cascade (testing, treatment, and viral suppression) are needed to assess the effectiveness of "test and treat" strategies implemented to achieve this goal. The data available to inform such estimates, however, are susceptible to informative missingness: the number of HIV-positive individuals in a population is unknown; individuals tested for HIV may not be representative of those whom a testing intervention fails to reach, and HIV-positive individuals with a viral …

Further Advances For The Sequential Multiple Assignment Randomized Trial (Smart), Tianjiao Dai

Dissertations and Theses (Open Access)

ABSTRACT

FURTHER ADVANCES FOR THE SEQUENTIAL MULTIPLE ASSIGNMENT RANDOMIZED TRIAL (SMART)

Tianjiao Dai, M.S.

Advisory Professor: Sanjay Shete, Ph.D.

Sequential multiple assignment randomized trial (SMART) designs have been developed these years for studying adaptive interventions. In my Ph.D. study, I mainly investigate how to further improve SMART designs and optimize the interventions for each individual in the trial. My dissertation has focused on two topics of SMART designs.

1) Developing a novel SMART design that can reduce the cost and side effects associated with the interventions and proposing the corresponding analytic methods. I have developed a time-varying SMART design in …

Bayesian Exponential Random Graph Modelling Of Interhospital Patient Referral Networks, Alberto Caimo, Francesca Pallotti, Alessandro Lomi

Articles

Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among healthcare organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well-known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described can be reproduced with accuracy by specifying the system …

Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret

UW Biostatistics Working Paper Series

We have frequently implemented crossover studies to evaluate new therapeutic interventions for genital herpes simplex virus infection. The outcome measured to assess the efficacy of interventions on herpes disease severity is the viral shedding rate, defined as the frequency of detection of HSV on the genital skin and mucosa. We performed a simulation study to ascertain whether our standard model, which we have used previously, was appropriately considering all the necessary features of the shedding data to provide correct inference. We simulated shedding data under our standard, validated assumptions and assessed the ability of 5 different models to reproduce the …

In-Shoe Plantar Pressure System To Investigate Ground Reaction Force Using Android Platform, Ahmed A. Mostfa

Theses and Dissertations

Human footwear is not yet designed to optimally relieve pressure on the heel of the foot. Proper foot pressure assessment requires personal training and measurements by specialized machinery. This research aims to investigate and hypothesize about Preferred Transition Speed (PTS) and to classify the gait phase of explicit variances in walking patterns between different subjects. An in-shoe wearable pressure system using Android application was developed to investigate walking patterns and collect data on Activities of Daily Living (ADL). In-shoe circuitry used Flexi-Force A201 sensors placed at three major areas: heel contact, 1st metatarsal, and 5th metatarsal with a PIC16F688 microcontroller …

Preparedness Of Hospitals In The Republic Of Ireland For An Influenza Pandemic, An Infection Control Perspective, Mary Reidy, Fiona Ryan, Dervla Hogan, Seán Lacey, Claire Buckley

Department of Mathematics Publications

When an influenza pandemic occurs most of the population is susceptible and attack rates can range as high as 40–50 %. The most important failure in pandemic planning is the lack of standards or guidelines regarding what it means to be ‘prepared’. The aim of this study was to assess the preparedness of acute hospitals in the Republic of Ireland for an influenza pandemic from an infection control perspective.

Comparing Partial Least Square Approaches In Gene-Or Region-Based Association Study For Multiple Quantitative Phenotypes, Zhongshang Yuan, Xiaoshuai Zhang, Fangyu Li, Jinghua Zhao, Fuzhong Xue

Human Biology Open Access Pre-Prints

On thinking quantitatively of complex diseases, there are at least three statistical strategies for association study: single SNP on single trait, gene-or region (with multiple SNPs) on single trait and on multiple traits. The third of which is the most general in dissecting the genetic mechanism underlying complex diseases underpinning multiple quantitative traits. Gene-or region association methods based on partial least square (PLS) approaches have been shown to have apparent power advantage. However, few attempts are developed for multiple quantitative phenotypes or traits underlying a condition or disease, and the performance of various PLS approaches used in association study for …

Adaptive Pair-Matching In The Search Trial And Estimation Of The Intervention Effect, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentially increase study power. In a common design, candidate units are identified, and their baseline characteristics used to create the best n/2 matched pairs. Within the resulting pairs, the intervention is randomized, and the outcomes measured at the end of follow-up. We consider this design to be adaptive, because the construction of the matched pairs depends on the baseline covariates of all candidate units. As consequence, the observed data cannot be considered as n/2 independent, identically distributed (i.i.d.) pairs of units, as current practice assumes. …

Meta-Analysis Of Social-Personality Psychological Research, Blair T. Johnson, Alice H. Eagly

CHIP Documents

This publication provides a contemporary treatment of the subject of meta-analysis in relation to social-personality psychology. Meta-analysis literally refers to the statistical pooling of the results of independent studies on a given subject, although in practice it refers as well to other steps of research synthesis, including defining the question under investigation, gathering all available research reports, coding of information about the studies and their effects, and interpretation/dissemination of results. Discussed as well are the hallmarks of high-quality meta-analyses.

Using The Inverse Transform To Specify Contrasts In Regression And Latent Curve Structural Equation Models, Thomas N. Templin

Nursing Faculty Research Publications

A simple yet general method for specifying contrasts to test hypotheses in regression and latent curve structural equation models is presented. The traditional qualitative variable coding schemes used in multiple regression (e.g., dummy coding) have a more general formulation. Five matrices are involved: The coding scheme, A. The matrix which gives the distribution and ordering of cases, W; WA = X; X is the design matrix. The contrast coefficient matrix C; and C^-1 = A. In practice, only C, C^-1, and A are necessary because the statistical software generates the design matrix. This method has great generality …

Ensemble-Based Methods For Forecasting Census In Hospital Units, Devin C. Koestler, Hernando Ombao, Jesse Bender

Dartmouth Scholarship

The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, …

Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study, Hong Zhu, Mei-Cheng Wang

Johns Hopkins University, Dept. of Biostatistics Working Papers

In disease surveillance systems or registries, bivariate survival data are typically collected under interval sampling. It refers to a situation when entry into a registry is at the time of the first failure event (e.g., HIV infection) within a calendar time interval, the time of the initiating event (e.g., birth) is retrospectively identified for all the cases in the registry, and subsequently the second failure event (e.g., death) is observed during the follow-up. Sampling bias is induced due to the selection process that the data are collected conditioning on the first failure event occurs within a time interval. Consequently, the …

Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr.

CHIP Documents

In any scientific discipline, the ability to portray research patterns graphically often aids greatly in interpreting a phenomenon. In part to depict phenomena, the statistics and capabilities of meta-analytic models have grown increasingly sophisticated. Accordingly, this article details how to move the constant in weighted meta-analysis regression models (viz. “meta-regression”) to illuminate the patterns in such models across a range of complexities. Although it is commonly ignored in practice, the constant (or intercept) in such models can be indispensible when it is not relegated to its usual static role. The moving constant technique makes possible estimates and confidence intervals at …

A Regularization Corrected Score Method For Nonlinear Regression Models With Covariate Error, David M. Zucker, Malka Gorfine, Yi Li, Donna Spiegelman

Harvard University Biostatistics Working Paper Series

No abstract provided.

Assessing Medicare Beneficiaries’ Strength‐Of‐Preference Scores For Health Care Options: How Engaging Does The Elicitation Technique Need To Be?, Trafford Crump, Hilary A. Llewellyn-Thomas

Dartmouth Scholarship

The objective was to determine if participants’ strength‐of‐preference scores for elective health care interventions at the end‐of‐life (EOL) elicited using a non‐engaging technique are affected by their prior use of an engaging elicitation technique.

Variable Importance Analysis With The Multipim R Package, Stephan J. Ritter, Nicholas P. Jewell, Alan E. Hubbard

U.C. Berkeley Division of Biostatistics Working Paper Series

We describe the R package multiPIM, including statistical background, functionality and user options. The package is for variable importance analysis, and is meant primarily for analyzing data from exploratory epidemiological studies, though it could certainly be applied in other areas as well. The approach taken to variable importance comes from the causal inference field, and is different from approaches taken in other R packages. By default, multiPIM uses a double robust targeted maximum likelihood estimator (TMLE) of a parameter akin to the attributable risk. Several regression methods/machine learning algorithms are available for estimating the nuisance parameters of the models, including …

Minimum Description Length And Empirical Bayes Methods Of Identifying Snps Associated With Disease, Ye Yang, David R. Bickel

COBRA Preprint Series

The goal of determining which of hundreds of thousands of SNPs are associated with disease poses one of the most challenging multiple testing problems. Using the empirical Bayes approach, the local false discovery rate (LFDR) estimated using popular semiparametric models has enjoyed success in simultaneous inference. However, the estimated LFDR can be biased because the semiparametric approach tends to overestimate the proportion of the non-associated single nucleotide polymorphisms (SNPs). One of the negative consequences is that, like conventional p-values, such LFDR estimates cannot quantify the amount of information in the data that favors the null hypothesis of no disease-association.

We …

Landmark Prediction Of Survival, Layla Parast, Tianxi Cai

Harvard University Biostatistics Working Paper Series

No abstract provided.

Comparison Of Non-Surgical And Surgical Endodontic Retreatment: A Systematic Review, Robert Corr

Loma Linda University Electronic Theses, Dissertations & Projects

Primary root canal therapy has been shown to be a predictable procedure with a high degree of success ^1-4, however failures can occur after treatment. Treatment options for the preservation of teeth that have had previous endodontic treatment but demonstrate persistent disease include non-surgical (orthograde) or surgical (retrograde) endodontic retreatment, assuming the tooth is restorable, periodontally sound, and the patient desires to retain the tooth. The purpose of this study was to conduct a systematic review of the current available evidence to compare the clinical and radiographic outcomes of nonsurgical with those of surgical endodontic retreatment. Methodology began with …

Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Survival Analysis With Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach, Xiaomei Liao, David M. Zucker, Yi Li, Donna Spiegelman

Harvard University Biostatistics Working Paper Series

No abstract provided.

Causal Inference For Nested Case-Control Studies Using Targeted Maximum Likelihood Estimation, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

A nested case-control study is conducted within a well-defined cohort arising out of a population of interest. This design is often used in epidemiology to reduce the costs associated with collecting data on the full cohort; however, the case control sample within the cohort is a biased sample. Methods for analyzing case-control studies have largely focused on logistic regression models that provide conditional and not marginal causal estimates of the odds ratio. We previously developed a Case-Control Weighted Targeted Maximum Likelihood Estimation (TMLE) procedure for case-control study designs, which relies on the prevalence probability q0. We propose the use of …

Comparing Risk Scoring Systems Beyond The Roc Paradigm In Survival Analysis, Hajime Uno, Lu Tian, Tianxi Cai, Isaac S. Kohane, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Spatial Cluster Detection For Repeatedly Measured Outcomes While Accounting For Residential History, Andrea J. Cook, Diane Gold, Yi Li

Harvard University Biostatistics Working Paper Series

No abstract provided.

Spatial Cluster Detection For Weighted Outcomes Using Cumulative Geographic Residuals, Andrea J. Cook, Yi Li, David Arterburn, Ram C. Tiwari

Harvard University Biostatistics Working Paper Series

No abstract provided.

The Importance Of Scale For Spatial-Confounding Bias And Precision Of Spatial Regression Estimators, Christopher J. Paciorek

Harvard University Biostatistics Working Paper Series

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias …

Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Group Comparison Of Eigenvalues And Eigenvectors Of Diffusion Tensors, Armin Schwartzman, Robert F. Dougherty, Jonathan E. Taylor

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Small Sample Correction For Estimating Attributable Risk In Case-Control Studies, Daniel B. Rubin

U.C. Berkeley Division of Biostatistics Working Paper Series

The attributable risk, often called the population attributable risk, is in many epidemiological contexts a more relevant measure of exposure-disease association than the excess risk, relative risk, or odds ratio. When estimating attributable risk with case-control data and a rare disease, we present a simple correction to the standard approach making it essentially unbiased, and also less noisy. As with analogous corrections given in Jewell (1986) for other measures of association, the adjustment often won't make a substantial difference unless the sample size is very small or point estimates are desired within fine strata, but we discuss the possible utility …