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Articles 1 - 11 of 11
Full-Text Articles in Clinical Trials
Tricyclic Antidepressant Use And Risk Of Fractures: A Meta-Analysis Of Cohort Studies Through The Use Of Both Frequentist And Bayesian Approaches, Qing Wu, Yingke Xu, Yueyang Bao, Jovan Alvarez, Mikee Lianne Gonzales
Tricyclic Antidepressant Use And Risk Of Fractures: A Meta-Analysis Of Cohort Studies Through The Use Of Both Frequentist And Bayesian Approaches, Qing Wu, Yingke Xu, Yueyang Bao, Jovan Alvarez, Mikee Lianne Gonzales
School of Medicine Faculty Research
Background: Research findings regarding the association between tricyclic antidepressant (TCA) treatment and the risk of fracture are not consistent; we aimed to assess whether people who take TCAs are at an increased fracture risk. Methods: Relevant studies published through June 2020 were identified through database searches of MEDLINE, EMBASE, Scopus, PsycINFO, ISI Web of Science, WorldCat Dissertations and Theses from each database’s inception, as well as through manual searches of relevant reference lists. Two researchers independently performed literature searches, study selection, data abstraction and study appraisal by using a standardized protocol. Frequentist and Bayesian hierarchical random-effects models were used for …
Utility Of Inflammatory Markers To Predict Adverse Outcome In Acute Pancreatitis: A Retrospective Study In A Single Academic Center, Mohamad Mubder, Banreet Dhindsa, Danny Nguyen, Syed Saghir, Chad Cross, Ranjit Makar, Gordon Ohning
Utility Of Inflammatory Markers To Predict Adverse Outcome In Acute Pancreatitis: A Retrospective Study In A Single Academic Center, Mohamad Mubder, Banreet Dhindsa, Danny Nguyen, Syed Saghir, Chad Cross, Ranjit Makar, Gordon Ohning
School of Medicine Faculty Research
Background/Aim: Acute pancreatitis (AP) is a commonly encountered emergency where early identification of complicated cases is important. Inflammatory markers like lymphocyte to monocyte ratio (LMR) and neutrophil to lymphocyte ratio (NLR) are simple and readily available markers. In this study, we evaluated the utility of these markers in the early identification of patients with complicated AP. Patients and Methods: All patients with a diagnosis of AP admitted to the University Medical Center in Las Vegas/Nevada between August 2015 and September 2018 were identified using ICD-10 codes. Medical records were reviewed retrospectively. Epidemiological measures and their associated confidence intervals were calculated …
Sensitivity Analysis For Incomplete Data And Causal Inference, Heng Chen
Sensitivity Analysis For Incomplete Data And Causal Inference, Heng Chen
Statistical Science Theses and Dissertations
In this dissertation, we explore sensitivity analyses under three different types of incomplete data problems, including missing outcomes, missing outcomes and missing predictors, potential outcomes in \emph{Rubin causal model (RCM)}. The first sensitivity analysis is conducted for the \emph{missing completely at random (MCAR)} assumption in frequentist inference; the second one is conducted for the \emph{missing at random (MAR)} assumption in likelihood inference; the third one is conducted for one novel assumption, the ``sixth assumption'' proposed for the robustness of instrumental variable estimand in causal inference.
Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya
Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya
Electronic Theses and Dissertations
Generalized linear models have broad applications in biostatistics and sociology. In a regression setup, the main target is to find a relevant set of predictors out of a large collection of covariates. Sparsity is the assumption that only a few of these covariates in a regression setup have a meaningful correlation with an outcome variate of interest. Sparsity is incorporated by regularizing the irrelevant slopes towards zero without changing the relevant predictors and keeping the resulting inferences intact. Frequentist variable selection and sparsity are addressed by popular techniques like Lasso, Elastic Net. Bayesian penalized regression can tackle the curse of …
A Modular Framework For Early-Phase Seamless Oncology Trials, Philip S. Boonstra, Thomas M. Braun, Elizabeth C. Chase
A Modular Framework For Early-Phase Seamless Oncology Trials, Philip S. Boonstra, Thomas M. Braun, Elizabeth C. Chase
The University of Michigan Department of Biostatistics Working Paper Series
Background: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now 'seamless', meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (i) what is the probability that the design will identify an acceptable, i.e. safe …
Integrated Multiple Adaptive Design Involving Sample Size Re-Estimation And (Covariate-Adjusted) Response-Adaptive Randomization For Continuous And Binary Outcomes, Christine M. Orndahl
Integrated Multiple Adaptive Design Involving Sample Size Re-Estimation And (Covariate-Adjusted) Response-Adaptive Randomization For Continuous And Binary Outcomes, Christine M. Orndahl
Theses and Dissertations
Historically, clinical trials have been performed based on decisions made prior to the start of the trial. Adaptive designs have been developed to provide increased flexibility, allowing pre-specified changes to occur based on interim data. Each adaptive design addresses a unique pitfall of a non-adaptive design, such as minimizing the chance of an under- or over-powered study by utilizing interim data to update the sample size estimate (sample size re-estimation) or increasing the ethical benefit of a trial by allocating more participants to the better performing treatment group ([covariate-adjusted] response-adaptive randomization). Additional benefit is attainable by combining more than one …
Estimation Of The Treatment Effect With Bayesian Adjustment For Covariates, Li Xu
Estimation Of The Treatment Effect With Bayesian Adjustment For Covariates, Li Xu
Theses and Dissertations--Statistics
The Bayesian adjustment for confounding (BAC) is a Bayesian model averaging method to select and adjust for confounding factors when evaluating the average causal effect of an exposure on a certain outcome. We extend the BAC method to time-to-event outcomes. Specifically, the posterior distribution of the exposure effect on a time-to-event outcome is calculated as a weighted average of posterior distributions from a number of candidate proportional hazards models, weighing each model by its ability to adjust for confounding factors. The Bayesian Information Criterion based on the partial likelihood is used to compare different models and approximate the Bayes factor. …
Generalization Of Kullback-Leibler Divergence For Multi-Stage Diseases: Application To Diagnostic Test Accuracy And Optimal Cut-Points Selection Criterion, Chen Mo
College of Graduate Studies: Theses & Dissertations
The Kullback-Leibler divergence (KL), which captures the disparity between two distributions, has been considered as a measure for determining the diagnostic performance of an ordinal diagnostic test. This study applies KL and further generalizes it to comprehensively measure the diagnostic accuracy test for multi-stage (K > 2) diseases, named generalized total Kullback-Leibler divergence (GTKL). Also, GTKL is proposed as an optimal cut-points selection criterion for discriminating subjects among different disease stages. Moreover, the study investigates a variety of applications of GTKL on measuring the rule-in/out potentials in the single-stage and multi-stage levels. Intensive simulation studies are conducted to compare the performance …
Three Essays On Health Economics And Policy Evaluation, Shishir Shakya
Three Essays On Health Economics And Policy Evaluation, Shishir Shakya
Graduate Theses, Dissertations, and Problem Reports (ETD)
This dissertation consists of three essays on the U.S. Health care policy. Each paragraph below refers to the three abstracts for the three chapters in this dissertation, respectively. I provide quantitative evidence on how much Prescription Drug Monitoring Programs (PDMPs) affects the retail opioid prescribing behaviors. Using the American Community Survey (ACS), I retrieve county-level high dimensional panel data set from 2010 to 2017. I employ three separate identification strategies: difference-in-difference, double selection post-LASSO, and spatial difference-in-difference. I compare how the retail opioid prescribing behaviors of counties, that are mandatory for prescribers to check the PDMP before prescribing controlled substances …
A Two-Stage Design For Comparing Binomial Treatments With A Standard, Cecelia K. Schmidt
A Two-Stage Design For Comparing Binomial Treatments With A Standard, Cecelia K. Schmidt
UNF Graduate Theses and Dissertations
We propose a method for comparing success rates of several populations among each other and against a desired standard success rate. This design is appropriate for a situation in which all experimental treatments have only two outcomes that can be considered “success”and “failure” respectively. The goal is to identify which treatment has the highest rate of success that is also higher than the desired standard. The design combines elements of both hypothesis testing and statistical selection. At the first stage, if none of the samples have a number of successes above the appropriate standard for the design, the experiment is …
Natural Lead-In Approaches To Response-Adaptive Allocation In Clinical Trials, Erin E. Donahue
Natural Lead-In Approaches To Response-Adaptive Allocation In Clinical Trials, Erin E. Donahue
Theses and Dissertations
Response-adaptive (RA) allocation designs can be implemented in clinical trials to skew the allocation of incoming subjects toward the better performing treatment group based on the previously accrued subjects' responses. These designs alleviate potential ethical concerns of equally allocating subjects in a trial when one treatment arm is inferior. The RA design can be generalized to include covariate information in the covariate-adjusted response-adaptive (CARA) design, which aims to maximize treatment successes conditional on a set of patient characteristics. While RA and CARA designs can improve the treatment of patients, they have unstable estimators and increased variability in early stages of …