Physical Sciences and Mathematics Commons^™

Regression-Based Methods For Dynamic Treatment Regimes With Mismeasured Covariates Or Misclassified Response, Dan Liu

Electronic Thesis and Dissertation Repository

The statistical study of dynamic treatment regimes (DTRs) focuses on estimating sequential treatment decision rules tailored to patient-level information across multiple stages of intervention. Regression-based methods in DTR have been studied in the literature with a critical assumption that all the observed variables are precisely measured. However, this assumption is often violated in many applications. One example is the STAR*D study, in which the patient's depressive score is subject to measurement error. In this thesis, we explore problems in the context of DTR with measurement error or misclassification considered in the observed data.

The first project deals with covariate measurement …

Incorporation And Measurement Of Uncertainty In Clustered And Spatial Data, Yuan Hong

Theses and Dissertations

Analyzing population representative datasets for local estimation and predictions over time is important for monitoring related public health issues, however, there are many statistical challenges associated with such analyses. Mixed effect models are one of the common options which can incorporate time and spatial effect in the model and related inference is well established.

In the first part of this dissertation, to estimate area-level prevalence using individuallevel data, small area estimation (SAE) with post-stratified mixed effect models were used where sampling weights were also incorporated into it. However, if poststratification which requires more computation effort can improve estimation accuracy is …

Classification With Measurement Error In Covariates Or Response, With Application To Prostate Cancer Imaging Study, Kexin Luo

Electronic Thesis and Dissertation Repository

The research is motivated by the prostate cancer imaging study conducted at the University of Western Ontario to classify cancer status using multiple in-vivo images. The prostate cancer histological image and the in-vivo images are subject to misalignment in the co-registration procedure, which can be viewed as measurement error in covariates or response. We investigate methods to correct this problem.

The first proposed method corrects the predicted class probability when the data has misclassified labels. The correction equation is derived from the relationship between the true response and the error-prone response. The probability for the observed class label is adjusted …

Mixtures-Of-Regressions With Measurement Error, Xiaoqiong Fang

Theses and Dissertations--Statistics

Finite Mixture model has been studied for a long time, however, traditional methods assume that the variables are measured without error. Mixtures-of-regression model with measurement error imposes challenges to the statisticians, since both the mixture structure and the existence of measurement error can lead to inconsistent estimate for the regression coefficients. In order to solve the inconsistency, We propose series of methods to estimate the mixture likelihood of the mixtures-of-regressions model when there is measurement error, both in the responses and predictors. Different estimators of the parameters are derived and compared with respect to their relative efficiencies. The simulation results …

Bayesian Inference On Quantile Regression-Based Mixed-Effects Joint Models For Longitudinal-Survival Data From Aids Studies, Hanze Zhang

USF Tampa Graduate Theses and Dissertations

In HIV/AIDS studies, viral load (the number of copies of HIV-1 RNA) and CD4 cell counts are important biomarkers of the severity of viral infection, disease progression, and treatment evaluation. Recently, joint models, which have the capability on the bias reduction and estimates' efficiency improvement, have been developed to assess the longitudinal process, survival process, and the relationship between them simultaneously. However, the majority of the joint models are based on mean regression, which concentrates only on the mean effect of outcome variable conditional on certain covariates. In fact, in HIV/AIDS research, the mean effect may not always be of …

Data Analysis And Study Design In The Presence Of Error-Prone Diagnostic Tests, Xiangdong Gu

Doctoral Dissertations

Interval censored time to event outcomes arise when a silent event of interest is known to have occurred within a specific time period, determined by the times of the last negative and first positive diagnostic tests. The four chapters comprising this thesis are tied together by a common theme in that the outcome of interest is an interval censored time to event random variable. In Chapter 1, we describe a stratified Weibull model appropriate for interval cen- sored outcomes and implement a new R package straweib. We compare the proposed approach with the log-linear form of the Weibull regression model …

Evaluating Predictors Of An Individual’S Dietary Intake Latent Value Under Different Mixed Models, Shuli Yu

Doctoral Dissertations

The accurate estimation of an individual’s usual dietary intake is important since the estimates are essential to uncover the diet-disease relationships. This study explores a more accurate method to estimate an individual’s latent value of usual dietary intake when it is repeatedly measured using a 24-hour dietary recall (24HR) and seven day dietary recall (7DDR), accounting for random measurement error and bias. The performance of the (empirical) predictor of subject’s latent value obtained under the finite population mixed model (FPMM) framework is compared with those obtained under the usual mixed model and the measurement error model through a simulation study. …

Disk Diffusion Breakpoint Determination Using A Bayesian Nonparametric Variation Of The Errors-In-Variables Model, Glen Richard Depalma

Open Access Dissertations

Drug dilution (MIC) and disk diffusion (DIA) are the two most common antimicrobial susceptibility tests used by hospitals and clinics to determine an unknown pathogen's susceptibility to various antibiotics. Both tests use breakpoints to classify the pathogen as either susceptible, indeterminant, or resistant to each drug under consideration. While the determination of these drug-specific MIC classification breakpoints is straightforward, determination of comparable DIA breakpoints is not. It is this issue that motivates this research.

Traditionally, the error-rate bounded (ERB) method has been used to calibrate the two tests. This procedure involves determining DIA breakpoints which minimize the observed discrepancies between …

Statistical Methods For Nonlinear Dynamic Models With Measurement Error Using The Ricker Model, David Joseph Resendes

Open Access Dissertations

In ecological population management, years of animal counts are fit to nonlinear, dynamic models (e.g. the Ricker model) because the values of the parameters are of interest. The yearly counts are subject to measurement error, which inevitably leads to biased estimates and adversely affects inference if ignored. In the literature, often convenient distribution assumptions are imposed, readily available estimated measurement error variances are not utilized, or the measurement error is ignored entirely. In this thesis, ways to estimate the parameters of the Ricker model and perform inference while accounting for measurement error are investigated where distribution assumptions are minimized and …

Survival Analysis Of Microarray Data With Microarray Measurement Subject To Measurement Error, Juan Xiong

Electronic Thesis and Dissertation Repository

Microarray technology is essentially a measurement tool for measuring expressions of genes, and this measurement is subject to measurement error. Gene expressions could be employed as predictors for patient survival, and the measurement error involved in the gene expression is often ignored in the analysis of microarray data in the literature. Efforts are needed to establish statistical method for analyzing microarray data without ignoring the error in gene expression. A typical microarray data set has a large number of genes far exceeding the sample size. Proper selection of survival relevant genes contributes to an accurate prediction model. We study the …

Shrinkage Estimation In Partially Linear Models With Measurement Error, Yifang Li

All Theses

In practice, measurement error in the covariates is often encountered. Measurement error has several effects when using ordinary least squares for the regression problems. In this thesis, we introduce the basic idea of correcting the bias caused by different types of measurement error. We then focus on the variable selection for partially linear models when some of the covariates are measured with additive errors. The bias caused by the measurement error is corrected by subtracting a bias correction term in the squared loss function. Adaptive LASSO is used for the variable selection procedure. The rate of convergence and the asymptotic …