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Articles 1 - 7 of 7
Full-Text Articles in Biostatistics
Optimizing Dynamic Treatment Regimes With Q-Learning: Complications Due To Error-Prone Data And Applications To Covid-19 Data, Yasin Khadem Charvadeh
Optimizing Dynamic Treatment Regimes With Q-Learning: Complications Due To Error-Prone Data And Applications To Covid-19 Data, Yasin Khadem Charvadeh
Electronic Thesis and Dissertation Repository
In this thesis, we employ statistical modeling and methods to examine COVID-19 data, and we develop new methods to address new issues that invalidate some standard methods.
In the first study, we employ semiparametric and nonparametric survival models as well as data visualization techniques to examine the epidemiological features of COVID-19. Based on our numerical results, the median incubation time is about 5 days, and the elders are more likely to have longer incubation periods.
In the second study, we use data from 175 countries and investigate possible factors associated with the case fatality rate (CFR) of COVID-19. The Q-learning …
Flexible Modelling Of Time-Dependent Covariate Effects With Correlated Competing Risks: Application To Hereditary Breast And Ovarian Cancer Families, Seungwoo Lee
Electronic Thesis and Dissertation Repository
This thesis aims to develop a flexible approach for modelling time-dependent covariate effects on event risk using B-splines in the presence of correlated competing risks. The performance of the proposed model was evaluated via simulation in terms of the bias and precision of the estimation of the parameters and penetrance functions. In addition, we extended the concordance index to account for time-dependent effects and competing events simultaneously and demonstrated its inference procedures. We applied our proposed methods to data rising from the BRCA1 mutation families from the breast cancer family registry to evaluate the time-dependent effects of mammographic screening and …
Addressing Bias In Non-Experimental Studies Assessing Treatment Outcomes In Prostate Cancer, David E. Guy
Addressing Bias In Non-Experimental Studies Assessing Treatment Outcomes In Prostate Cancer, David E. Guy
Electronic Thesis and Dissertation Repository
We evaluated the ability of matching techniques to balance baseline characteristics between treatment groups using non-experimental data. We identified a set of balance diagnostics that assessed key differences in baseline covariates with potential for confounding. These diagnostics were used in a novel systematic approach to developing and evaluating models for use in propensity score matching that optimized balance and data retention. We then compared the performance of propensity score and coarsened exact matching strategies in optimizing balance and data retention, using non-experimental data from a pan-Canadian prostate cancer database. Both matching techniques balanced baseline covariates adequately and retained approximately 70% …
On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, Daniel Prawira
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 …
Joint Modelling In Liver Transplantation, Elizabeth M. Renouf
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 …
Flexible Partially Linear Single Index Regression Models For Multivariate Survival Data, Na Lei
Flexible Partially Linear Single Index Regression Models For Multivariate Survival Data, Na Lei
Electronic Thesis and Dissertation Repository
Survival regression models usually assume that covariate effects have a linear form. In many circumstances, however, the assumption of linearity may be violated. The present work addresses this limitation by adding nonlinear covariate effects to survival models. Nonlinear covariates are handled using a single index structure, which allows high-dimensional nonlinear effects to be reduced to a scalar term. The nonlinear single index approach is applied to modeling of survival data with multivariate responses, in three popular models: the proportional hazards (PH) model, the proportional odds (PO) model, and the generalized transformation model. Another extension of the PH and PO model …
Survival Analysis Of Microarray Data With Microarray Measurement Subject To Measurement Error, Juan Xiong
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 …