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On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, Daniel Prawira 2017 The University of Western Ontario

On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, Daniel Prawira

Electronic Thesis and Dissertation Repository

In family studies, we are interested in estimating the penetrance function of the event of interest in the presence of competing risks. Failure to account for competing risks may lead to bias in the estimation of the penetrance function. In this thesis, three statistical challenges are addressed: clustering, missing data, and competing risks. We proposed the cause-specific model with shared frailty and ascertainment correction to account for clustering and competing risks along with ascertainment of families into study. Multiple imputation is used to account for missing data. The simulation study showed good performance of our proposed model in estimating the ...


Comparision Of Survival Curves Between Cox Proportional Hazards, Random Forests, And Conditional Inference Forests In Survival Analysis, Brandon Weathers, Richard Cutler Dr. 2017 Utah State University

Comparision Of Survival Curves Between Cox Proportional Hazards, Random Forests, And Conditional Inference Forests In Survival Analysis, Brandon Weathers, Richard Cutler Dr.

All Graduate Plan B and other Reports

Survival analysis methods are a mainstay of the biomedical fields but are finding increasing use in other disciplines including finance and engineering. A widely used tool in survival analysis is the Cox proportional hazards regression model. For this model, all the predicted survivor curves have the same basic shape, which may not be a good approximation to reality. In contrast the Random Survival Forests does not make the proportional hazards assumption and has the flexibility to model survivor curves that are of quite different shapes for different groups of subjects. We applied both techniques to a number of publicly available ...


Sidz Dc Article April Twenty, Sidney Twentythree Sr., Ann Test 2017 bepress university libraries

Sidz Dc Article April Twenty, Sidney Twentythree Sr., Ann Test

Ann Test

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Novel Computational Methods For Censored Data And Regression, Yifan Yang 2017 University of Kentucky

Novel Computational Methods For Censored Data And Regression, Yifan Yang

Theses and Dissertations--Statistics

This dissertation can be divided into three topics. In the first topic, we derived a recursive algorithm for the constrained Kaplan-Meier estimator, which promotes the computation speed up to fifty times compared to the current method that uses EM algorithm. We also showed how this leads to the vast improvement of empirical likelihood analysis with right censored data. After a brief review of regularized regressions, we investigated the computational problems in the parametric/non-parametric hybrid accelerated failure time models and its regularization in a high dimensional setting. We also illustrated that, when the number of pieces increases, the discussed models ...


Survival Analysis: A Modified Kaplan-Meir Estimator, Justin A. Bancroft 2017 Missouri State University

Survival Analysis: A Modified Kaplan-Meir Estimator, Justin A. Bancroft

MSU Graduate Theses

The popular Kaplan-Meir estimator has traditionally been used to great effect as a survival function estimator. However, the Kaplan-Meir estimator is dependent upon a maximum likelihood parameter estimator which may not be the best estimator in all cases. We modify the Kaplan-Meir estimator, based on a Bayes parameter estimation, in hopes of providing a more accurate survival estimator for small sample sizes. Core elements of survival analysis are presented, acting as a foundation from which to construct and compare our modified Kaplan-Meir estimator. It is hypothesized that our modified Kaplan-Meir estimator is generally more accurate than the standard Kaplan-Meir estimator ...


Survival Analysis In A Clinical Setting, Yunzhao Liu 2016 Washington University in St. Louis

Survival Analysis In A Clinical Setting, Yunzhao Liu

Arts & Sciences Electronic Theses and Dissertations

With the fast paced advancement of modern medicine, cancer treatments have improved greatly over the past few decades; however, the overall survival rate has not improved for head neck squamous cell carcinoma (HNSCC). Traditionally, the general affected population of HNSCC was male over 50-60 years of age, whom have had history of alcohol and tobacco use. Conversely, in the recent decades, HNSCC has exhibited significant rise in younger patients, largely due to the increase in human papillomavirus (HPV) infection among young adults.

Generally, HPV as the most prevalent sexually transmitted disease, consisted of strains that do not cause harm to ...


Joint Modelling In Liver Transplantation, Elizabeth M. Renouf 2016 The University of Western Ontario

Joint Modelling In Liver Transplantation, Elizabeth M. Renouf

Electronic Thesis and Dissertation Repository

In the setting of liver transplantation, clinical trials and transplant registries regularly collect repeated measurements of clinical biomarkers which may be strongly associated with a time-to-event such as graft failure or disease recurrence. Multiple time-to-event outcomes are routinely collected. However, joint models are rarely used. This thesis will describe important considerations for joint modelling in the setting of liver transplantation. We will focus on transplant registry data from the United States. We develop a new tool for joint modelling in the context where a critical health event can be tracked in the longitudinal biomarker and often presents as a non-linear ...


Another Coauthor Test Here, Sid Twentythree, Sid B. Testeroni 2016 bepress university

Another Coauthor Test Here, Sid Twentythree, Sid B. Testeroni

Sid Testeroni

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Conditional Screening For Ultra-High Dimensional Covariates With Survival Outcomes, Hyokyoung Grace Hong, Jian Kang, Yi Li 2016 Michigan State University

Conditional Screening For Ultra-High Dimensional Covariates With Survival Outcomes, Hyokyoung Grace Hong, Jian Kang, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in ...


Test Two - Thursday, Sid Twomarchsixteen 2016 Selected Works

Test Two - Thursday, Sid Twomarchsixteen

Sid Twomarchsixteen

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Multilevel Analysis Of Individual, Neighborhood, And Health Care Facility Characteristics Associated With Achievement And Maintenance Of Hiv Viral Suppression Among Persons Newly Diagnosed With Hiv In New York City, Ellen W. Wiewel 2016 Graduate Center, City University of New York

Multilevel Analysis Of Individual, Neighborhood, And Health Care Facility Characteristics Associated With Achievement And Maintenance Of Hiv Viral Suppression Among Persons Newly Diagnosed With Hiv In New York City, Ellen W. Wiewel

All Dissertations, Theses, and Capstone Projects

Objective

To investigate the effect of individual, health care facility, and neighborhood characteristics on achievement and maintenance of HIV viral suppression, among New York City residents aged 13 years and older diagnosed with HIV between 2006 and 2012.

Methods

I used individual-level data from the New York City HIV surveillance registry and Case Surveillance-Based Sampling, facility-level data from the surveillance registry, and neighborhood-level data from the U.S. Census and American Community Survey. The outcomes of interest were first viral suppression after diagnosis (Aims 1 and 3; ≤400 copies/mL) and virologic failure after first suppression among persons who achieved ...


Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret 2016 University of Washington - Seattle Campus

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 ...


Empirical Likelihood And Differentiable Functionals, Zhiyuan Shen 2016 University of Kentucky

Empirical Likelihood And Differentiable Functionals, Zhiyuan Shen

Theses and Dissertations--Statistics

Empirical likelihood (EL) is a recently developed nonparametric method of statistical inference. It has been shown by Owen (1988,1990) and many others that empirical likelihood ratio (ELR) method can be used to produce nice confidence intervals or regions. Owen (1988) shows that -2logELR converges to a chi-square distribution with one degree of freedom subject to a linear statistical functional in terms of distribution functions. However, a generalization of Owen's result to the right censored data setting is difficult since no explicit maximization can be obtained under constraint in terms of distribution functions. Pan and Zhou (2002), instead ...


Statistical Methods For Environmental Exposure Data Subject To Detection Limits, Yuchen Yang 2016 University of Kentucky

Statistical Methods For Environmental Exposure Data Subject To Detection Limits, Yuchen Yang

Theses and Dissertations--Statistics

In this dissertation, we develop unified and efficient nonparametric statistical methods for estimating and comparing environmental exposure distributions in presence of detection limits. In the first part, we propose a kernel-smoothed nonparametric estimator for the exposure distribution without imposing any independence assumption between the exposure level and detection limit. We show that the proposed estimator is consistent and asymptotically normal. Simulation studies demonstrate that the proposed estimator performs well in practical situations. A colon cancer study is provided for illustration. In the second part, we develop a class of test statistics to compare exposure distributions between two groups by using ...


Who Is Like Whom? Reclassification And Performance Patterns For Different Groupings Of English Learners, Molly M. Faulkner-Bond 2016 University of Massachusetts Amherst

Who Is Like Whom? Reclassification And Performance Patterns For Different Groupings Of English Learners, Molly M. Faulkner-Bond

Doctoral Dissertations

Approximately 10 percent of the US K-12 population consists of English learners (ELs), or students who are learning English in addition to academic content in areas like English language arts (ELA) and mathematics. In addition to meeting the same academic content and performance standards set for all students, it is also a goal for ELs to be reclassified – i.e., to master English so that they can shed the EL label and participate in academic settings where English is used without needing special support. Working with a longitudinal cohort of ~28,000 ELs in grades 3 through 8 from one ...


Introduction To The Analysis Of Survival Data In The Presence Of Competing Risks, Peter Austin, Douglas Lee, Jason Fine 2015 Institute for Clinical Evaluative Sciences

Introduction To The Analysis Of Survival Data In The Presence Of Competing Risks, Peter Austin, Douglas Lee, Jason Fine

Peter Austin

Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. When fitting regression ...


Doubly Robust And Efficient Estimation Of Marginal Structural Models For The Hazard Function, Wenjing Zheng, Maya Petersen, Mark van der Laan 2015 University of California, Berkeley

Doubly Robust And Efficient Estimation Of Marginal Structural Models For The Hazard Function, Wenjing Zheng, Maya Petersen, Mark Van Der Laan

Wenjing Zheng

In social and health sciences, many research questions involve understanding the causal effect of a longitudinal treatment on mortality (or time-to-event outcomes in general). Often, treatment status may change in response to past covariates that are risk factors for mortality, and in turn, treatment status may also affect such subsequent covariates. In these situations, Marginal Structural Models (MSMs), introduced by Robins (1997), are well-established and widely used tools to account for time-varying confounding. In particular, a MSM can be used to specify the intervention-specific counterfactual hazard function, i.e. the hazard for the outcome of a subject in an ideal ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


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