Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations,
2010
University of Michigan
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.
Utilizing The Integrated Difference Of Two Survival Functions To Quantify The Treatment Contrast For Designing, Monitoring And Analyzing A Comparative Clinical Study,
2010
Harvard University
Utilizing The Integrated Difference Of Two Survival Functions To Quantify The Treatment Contrast For Designing, Monitoring And Analyzing A Comparative Clinical Study, Lihui Zhao, Lu Tian, Hajime Uno, Scott D. Solomon, Marc A. Pfeffer, J. S. Schindler, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Likelihood Ratio Testing For Admixture Models With Application To Genetic Linkage Analysis,
2010
Fred Hutchinson Cancer Research Center
Likelihood Ratio Testing For Admixture Models With Application To Genetic Linkage Analysis, Chong-Zhi Di, Kung-Yee Liang
Johns Hopkins University, Dept. of Biostatistics Working Papers
We consider likelihood ratio tests (LRT) and their modifications for homogeneity in admixture models. The admixture model is a special case of two component mixture model, where one component is indexed by an unknown parameter while the parameter value for the other component is known. It has been widely used in genetic linkage analysis under heterogeneity, in which the kernel distribution is binomial. For such models, it is long recognized that testing for homogeneity is nonstandard and the LRT statistic does not converge to a conventional 2 distribution. In this paper, we investigate the asymptotic behavior of the LRT for …
Bayesian And Frequentist Approaches For The Analysis Of Multiple Endpoints Data Resulting From Exposure To Multiple Health Stressors.,
2010
Virginia Commonwealth University
Bayesian And Frequentist Approaches For The Analysis Of Multiple Endpoints Data Resulting From Exposure To Multiple Health Stressors., Epiphanie Nyirabahizi
Theses and Dissertations
In risk analysis, Benchmark dose (BMD)methodology is used to quantify the risk associated with exposure to stressors such as environmental chemicals. It consists of fitting a mathematical model to the exposure data and the BMD is the dose expected to result in a pre-specified response or benchmark response (BMR). Most available exposure data are from single chemical exposure, but living objects are exposed to multiple sources of hazards. Furthermore, in some studies, researchers may observe multiple endpoints on one subject. Statistical approaches to address multiple endpoints problem can be partitioned into a dimension reduction group and a dimension preservative group. …
Targeted Maximum Likelihood Method For Repeated Measures Semiparametric Regression: Discovery For Transcription Factor Activity,
2010
Division of Biostatistics, School of Public Health, University of California, Berkeley
Targeted Maximum Likelihood Method For Repeated Measures Semiparametric Regression: Discovery For Transcription Factor Activity, Catherine Tuglus, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
In longitudinal and repeated measures data analysis, often the goal is to determine the effect of a treatment or aspect on a particular outcome (e.g. disease progression). We consider semiparametric repeated measures regression model, where the parametric component models effect of the variable of interest and any modification by other covariates. The expectation of this parametric component over the other covariates is a measure of variable importance. Here we present a targeted maximum likelihood estimator of the finite dimensional regression parameter, which is easily estimated using standard software for generalized estimating equations. The targeted maximum likelihood method provides double robust …
Graphical Procedures For Evaluating Overall And Subject-Specific Incremental Values From New Predictors With Censored Event Time Data,
2010
Dana Farber Cancer Institute
Graphical Procedures For Evaluating Overall And Subject-Specific Incremental Values From New Predictors With Censored Event Time Data, Hajime Uno, Tianxi Cai, Lu Tian, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Doubly Regularized Reml For Estimation And Selection Of Fixed And Random Effects In Linear Mixed-Effects Models,
2010
University of Michigan
Doubly Regularized Reml For Estimation And Selection Of Fixed And Random Effects In Linear Mixed-Effects Models, Sijian Wang, Peter Xuewin Song, Ji Zhu
The University of Michigan Department of Biostatistics Working Paper Series
The linear mixed effects model (LMM) is widely used in the analysis of clustered or longitudinal data. In the practice of LMM, the inference on the structure of the random effects component is of great importance, not only to yield proper interpretation of subject-specific effects but also to draw valid statistical conclusions. This task of inference becomes significantly challenging when a large number of fixed effects and random effects are involved in the analysis. The difficulty of variable selection arises from the need of simultaneously regularizing both mean model and covariance structures, with possible parameter constraints between the two. In …
Multilevel Sparse Functional Principal Component Analysis,
2010
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center
Multilevel Sparse Functional Principal Component Analysis, Chong-Zhi Di, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
The basic observational unit in this paper is a function. Data are assumed to have a natural hierarchy of basic units. A simple example is when functions are recorded at multiple visits for the same subject. Di et al. (2009) proposed Multilevel Functional Principal Component Analysis (MFPCA) for this type of data structure when functions are densely sampled. Here we consider the case when functions are sparsely sampled and may contain as few as 2 or 3 observations per function. As with MFPCA, we exploit the multilevel structure of covariance operators and data reduction induced by the use of principal …
Targeting The Optimal Design In Randomized Clinical Trials With Binary Outcomes And No Covariate,
2010
Laboratoire MAP5, Université Paris Descartes and CNRS
Targeting The Optimal Design In Randomized Clinical Trials With Binary Outcomes And No Covariate, Antoine Chambaz, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
This article is devoted to the asymptotic study of adaptive group sequential designs in the case of randomized clinical trials with binary treatment, binary outcome and no covariate. By adaptive design, we mean in this setting a clinical trial design that allows the investigator to dynamically modify its course through data-driven adjustment of the randomization probability based on data accrued so far, without negatively impacting on the statistical integrity of the trial. By adaptive group sequential design, we refer to the fact that group sequential testing methods can be equally well applied on top of adaptive designs. Prior to collection …
Targeted Maximum Likelihood Based Causal Inference,
2010
University of California - Berkeley
Targeted Maximum Likelihood Based Causal Inference, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Given causal graph assumptions, intervention-specific counterfactual distributions of the data can be defined by the so called G-computation formula, which is obtained by carrying out these interventions on the likelihood of the data factorized according to the causal graph. The obtained G-computation formula represents the counterfactual distribution the data would have had if this intervention would have been enforced on the system generating the data. A causal effect of interest can now be defined as some difference between these counterfactual distributions indexed by different interventions. For example, the interventions can represent static treatment regimens or individualized treatment rules that assign …
Bio-Creep In Non-Inferiority Clinical Trials,
2010
University of Washington - Seattle Campus
Bio-Creep In Non-Inferiority Clinical Trials, Siobhan P. Everson-Stewart, Scott S. Emerson
UW Biostatistics Working Paper Series
After a non-inferiority clinical trial, a new therapy may be accepted as effective, even if its treatment effect is slightly smaller than the current standard. It is therefore possible that, after a series of trials where the new therapy is slightly worse than the preceding drugs, an ineffective or harmful therapy might be incorrectly declared efficacious; this is known as “bio-creep.” Several factors may influence the rate at which bio-creep occurs, including the distribution of the effects of the new agents being tested and how that changes over time, the choice of active comparator, the method used to model the …
Estimates Of Information Growth In Longitudinal Clinical Trials,
2010
University of Washington
Estimates Of Information Growth In Longitudinal Clinical Trials, Abigail Shoben, Kyle Rudser, Scott S. Emerson
UW Biostatistics Working Paper Series
In group sequential clinical trials, it is necessary to estimate the amount of information present at interim analysis times relative to the amount of information that would be present at the final analysis. If only one measurement is made per individual, this is often the ratio of sample sizes available at the interim and final analyses. However, as discussed by Wu and Lan (1992), when the statistic of interest is a change over time, as with longitudinal data, such an approach overstates the information. In this paper, we discuss other problems that can result in overestimating the information, such as …
Simple, Efficient Estimators Of Treatment Effects In Randomized Trials Using Generalized Linear Models To Leverage Baseline Variables,
2010
Johns Hopkins University
Simple, Efficient Estimators Of Treatment Effects In Randomized Trials Using Generalized Linear Models To Leverage Baseline Variables, Michael Rosenblum, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we …
Targeted Maximum Likelihood Estimation Of The Parameter Of A Marginal Structural Model,
2010
Johns Hopkins University
Targeted Maximum Likelihood Estimation Of The Parameter Of A Marginal Structural Model, Michael Rosenblum, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Targeted maximum likelihood estimation is a versatile tool for estimating parameters in semiparametric and nonparametric models. We work through an example applying targeted maximum likelihood methodology to estimate the parameter of a marginal structural model. In the case we consider, we show how this can be easily done by clever use of standard statistical software. We point out differences between targeted maximum likelihood estimation and other approaches (including estimating function based methods). The application we consider is to estimate the effect of adherence to antiretroviral medications on virologic failure in HIV positive individuals.
Identification Of Neuroblastoma And Its Prognostic Markers Using Raman Spectroscopy,
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 …
Detecting Outliers And Influential Observations In Survival Model.,
2010
Universiti Malaya
Detecting Outliers And Influential Observations In Survival Model., Nor Akmal Md Noh
Student Works (2010-2019)
This study proposes outlier and influential observation detection procedures for Cox proportional hazard model. In the estimation process, the parameters for Cox proportional hazard model are estimated using partial likelihood method, while the baseline hazard estimates are obtained using Nelson-Aalen method. The procedure of outlier detection is based on three types of residuals; deviance, log-odd and normal deviate residuals. We study their properties and compare their performance in detecting outliers via simulation. On the other hand, we propose a procedure of identifying influential observation using forward search method. The method has been shown to be effective in detecting influential observations …
On The Eigenstructures Of Functional K-Potent Matrices And Their Integral Forms,
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.
A Markov Transition Model To Dementia With Death As A Competing Event,
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 …
