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Accounting For Response Misclassification And Covariate Measurement Error Improves Powers And Reduces Bias In Epidemiologic Studies, Dunlei Cheng, Adam J. Branscum, James D. Stamey 2010 Baylor Health Care System

Accounting For Response Misclassification And Covariate Measurement Error Improves Powers And Reduces Bias In Epidemiologic Studies, Dunlei Cheng, Adam J. Branscum, James D. Stamey

Dunlei Cheng

Purpose: To quantify the impact of ignoring misclassification of a response variable and measurement error in a covariate on statistical power, and to develop software for sample size and power analysis that accounts for these flaws in epidemiologic data. Methods: A Monte Carlo simulation-based procedure is developed to illustrate the differences in design requirements and inferences between analytic methods that properly account for misclassification and measurement error to those that do not in regression models for cross-sectional and cohort data. Results: We found that failure to account for these flaws in epidemiologic data can lead to a substantial reduction in ...


A Bayesian Approach To Sample Size Determination For Studies Designed To Evaluate Continuous Medical Tests, Dunlei Cheng, Adam J. Branscum, James D. Stamey 2010 Baylor Health Care System

A Bayesian Approach To Sample Size Determination For Studies Designed To Evaluate Continuous Medical Tests, Dunlei Cheng, Adam J. Branscum, James D. Stamey

Dunlei Cheng

We develop a Bayesian approach to sample size and power calculations for cross-sectional studies that are designed to evaluate and compare continuous medical tests. For studies that involve one test or two conditionally independent or dependent tests, we present methods that are applicable when the true disease status of sampled individuals will be available and when it will not. Within a hypothesis testing framework, we consider the goal of demonstrating that a medical test has area under the receiver operating characteristic (ROC) curve that exceeds a minimum acceptable level or another relevant threshold, and the goals of establishing the superiority ...


Statistical Criteria For Selecting The Optimal Number Of Untreated Subjects Matched To Each Treated Subject When Using Many-To-One Matching On The Propensity Score, Peter C. Austin 2010 Institute for Clinical Evaluative Sciences

Statistical Criteria For Selecting The Optimal Number Of Untreated Subjects Matched To Each Treated Subject When Using Many-To-One Matching On The Propensity Score, Peter C. Austin

Peter Austin

Propensity-score matching is increasingly being used to estimate the effects of treatments using observational data. In many-to-one (M:1) matching on the propensity score, M untreated subjects are matched to each treated subject using the propensity score. The authors used Monte Carlo simulations to examine the effect of the choice of M on the statistical performance of matched estimators. They considered matching 1–5 untreated subjects to each treated subject using both nearest-neighbor matching and caliper matching in 96 different scenarios. Increasing the number of untreated subjects matched to each treated subject tended to increase the bias in the estimated ...


The Performance Of Different Propensity-Score Methods For Estimating Differences In Proportions (Risk Differences Or Absolute Risk Reductions) In Observational Studies, Peter C. Austin 2010 Institute for Clinical Evaluative Sciences

The Performance Of Different Propensity-Score Methods For Estimating Differences In Proportions (Risk Differences Or Absolute Risk Reductions) In Observational Studies, Peter C. Austin

Peter Austin

Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical ...


Statistical Modeling Of Agatston Score In Multi-Ethnic Study Of Atherosclerosis (Mesa), Anna Liu, S. Ma, J. Carr, W. Post, R. Kronmal 2010 University of Massachusetts - Amherst

Statistical Modeling Of Agatston Score In Multi-Ethnic Study Of Atherosclerosis (Mesa), Anna Liu, S. Ma, J. Carr, W. Post, R. Kronmal

Anna Liu

The MESA (Multi-Ethnic Study of Atherosclerosis) is an ongoing study of the prevalence, risk factors, and progression of subclinical cardiovascular disease in a multi-ethnic cohort. It provides a valuable opportunity to examine the development and progression of CAC (coronary artery calcium), which is an important risk factor for the development of coronary heart disease. In MESA, about half of the CAC scores are zero and the rest are continuously distributed. Such data has been referred to as “zero-inflated data” and may be described using two-part models. Existing two-part model studies have limitations in that they usually consider parametric models only ...


A Markov Transition Model To Dementia With Death As A Competing Event, Liou Xu 2010 University of Kentucky

A Markov Transition Model To Dementia With Death As A Competing Event, Liou Xu

University of Kentucky Doctoral Dissertations

The research on multi-state Markov transition model is motivated by the nature of the longitudinal data from the Nun Study (Snowdon, 1997), and similar information on the BRAiNS cohort (Salazar, 2004). Our goal is to develop a flexible methodology for handling the categorical longitudinal responses and competing risks time-to-event that characterizes the features of the data for research on dementia. To do so, we treat the survival from death as a continuous variable rather than defining death as a competing absorbing state to dementia. We assume that within each subject the survival component and the Markov process are linked by ...


Identification Of Neuroblastoma And Its Prognostic Markers Using Raman Spectroscopy, Rachel Kast 2010 Wayne State University

Identification Of Neuroblastoma And Its Prognostic Markers Using Raman Spectroscopy, Rachel Kast

Wayne State University Dissertations

Introduction: Neuroblastoma is the most common cancer of infancy. It is one of several peripheral nervous system tumors, including ganglioneuroma, peripheral nerve sheath tumor, and pheochromocytoma. It is commonly situated on the adrenal gland. It displays similar histology to other small round blue cell tumors, including non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma. One method of judging neuroblastoma aggressiveness uses tumor histology factors, including mitosis-karyorrhexis index, Schwannian stromal development, degree of differentiation, and patient age. Tumor aggressiveness can also be judged based on the amplification of certain genes, including MYCN. Raman spectroscopy is a physics-based method which identifies the biochemical fingerprint ...


On The Eigenstructures Of Functional K-Potent Matrices And Their Integral Forms, Yan Wu, Daniel F. Linder 2010 Georgia Southern University

On The Eigenstructures Of Functional K-Potent Matrices And Their Integral Forms, Yan Wu, Daniel F. Linder

Biostatistics Faculty Publications

In this paper, a functional k-potent matrix satisfies the equation, where k and r are positive integers, and are real numbers. This class of matrices includes idempotent, Nilpotent, and involutary matrices, and more. It turns out that the matrices in this group are best distinguished by their associated eigen-structures. The spectral properties of the matrices are exploited to construct integral k-potent matrices, which have special roles in digital image encryption.


Measuring The Hiv/Aids Epidemic: Approaches And Challenges, Ron Brookmeyer 2009 University of California, Los Angeles

Measuring The Hiv/Aids Epidemic: Approaches And Challenges, Ron Brookmeyer

Ron Brookmeyer

In this article, the author reviews current approaches and methods for measuring the scope of the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemic and their strengths and weaknesses. In recent years, various public health agencies have revised statistical estimates of the scope of the HIV/AIDS pandemic. The author considers the reasons underlying these revisions. New sources of data for estimating HIV prevalence have become available, such as nationally representative probability-based surveys. New technologies such as biomarkers that indicate when persons became infected are now used to determine HIV incidence rates. The author summarizes the main sources of ...


Fast Function-On-Scalar Regression With Penalized Basis Expansions, Philip T. Reiss, Lei Huang, Maarten Mennes 2009 New York University

Fast Function-On-Scalar Regression With Penalized Basis Expansions, Philip T. Reiss, Lei Huang, Maarten Mennes

Lei Huang

Regression models for functional responses and scalar predictors are often fitted by means of basis functions, with quadratic roughness penalties applied to avoid overfitting. The fitting approach described by Ramsay and Silverman in the 1990s amounts to a penalized ordinary least squares (P-OLS) estimator of the coefficient functions. We recast this estimator as a generalized ridge regression estimator, and present a penalized generalized least squares (P-GLS) alternative. We describe algorithms by which both estimators can be implemented, with automatic selection of optimal smoothing parameters, in a more computationally efficient manner than has heretofore been available. We discuss pointwise confidence intervals ...


On The Statistical Accuracy Of Biomarker Assays Of Hiv Incidence, Ron Brookmeyer 2009 University of California, Los Angeles

On The Statistical Accuracy Of Biomarker Assays Of Hiv Incidence, Ron Brookmeyer

Ron Brookmeyer

Objective: To evaluate the statistical accuracy of estimates of current HIV incidence rates from cross-sectional surveys, and to identify characteristics of assays that improve accuracy.

Methods: Performed mathematical and statistical analysis of the cross-sectional estimator of HIV incidence to evaluate bias and variance. Developed probability models to evaluate impact of long tails of the window period distribution on accuracy.

Results: The standard cross-sectional estimate of HIV incidence rate is estimating a time-lagged incidence where the lag time, called the shadow, depends on the mean and the coefficient of variation of window periods. Equations show how the shadow increases with the ...


Semiparametric Analysis Of Recurrent Events: Artificial Censoring, Truncation, Pairwise Estimation And Inference, Debashis Ghosh 2009 Penn State University

Semiparametric Analysis Of Recurrent Events: Artificial Censoring, Truncation, Pairwise Estimation And Inference, Debashis Ghosh

Debashis Ghosh

The analysis of recurrent failure time data from longitudinal studies can be complicated by the presence of dependent censoring. There has been a substantive literature that has developed based on an artificial censoring device. We explore in this article the connection between this class of methods with truncated data structures. In addition, a new procedure is developed for estimation and inference in a joint model for recurrent events and dependent censoring. Estimation proceeds using a mixed U-statistic based estimating function approach. New resampling-based methods for variance estimation and model checking are also described. The methods are illustrated by application to ...


Fast Function-On-Scalar Regression With Penalized Basis Expansions, Philip T. Reiss, Lei Huang, Maarten Mennes 2009 New York University

Fast Function-On-Scalar Regression With Penalized Basis Expansions, Philip T. Reiss, Lei Huang, Maarten Mennes

Philip T. Reiss

Regression models for functional responses and scalar predictors are often fitted by means of basis functions, with quadratic roughness penalties applied to avoid overfitting. The fitting approach described by Ramsay and Silverman in the 1990s amounts to a penalized ordinary least squares (P-OLS) estimator of the coefficient functions. We recast this estimator as a generalized ridge regression estimator, and present a penalized generalized least squares (P-GLS) alternative. We describe algorithms by which both estimators can be implemented, with automatic selection of optimal smoothing parameters, in a more computationally efficient manner than has heretofore been available. We discuss pointwise confidence intervals ...


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