Bayesian Analysis for Sparse Functional Data

This dissertation mainly presents a novel Bayesian method for sparse functional data. Specifically, two models are proposed, one of which models all individual functions with a common smoothness and the other groups individual functions with heterogeneous smoothness. The proposed method utilizes the mixed effects model representation of the penalized splines for both the mean function and the individual functions. Given noninformative or weakly informative priors, Bayesian inference on the proposed models are developed and computations are done by using Markov Chain Monte Carlo (MCMC) methods. It has been shown that the proposed Bayesian methods perform well on irregularly spaced sparse functional data, where a traditional mixed eects model may often fail. This dissertation also includes a small section onorthogonal series functional estimation for density functions.Statistic

Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions

Objective: The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies. Methods: We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes. Results: A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9, with various effect sizes. The power to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models. Conclusion: This article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented

Neutral diagnosis: An innovative concept for medical device clinical trials

Study design and statistical analysis are crucial in pivotal clinical trials to evaluate the effectiveness and safety of new medical devices under investigation. In recent years, innovative intraoperative in vivo breast tumor diagnostic devices have been proposed to improve the accuracy and surgical outcomes of breast tumor patients undergoing resection. Although such technologies are promising, investigators need to obtain statistical evidence for the effectiveness and safety of these devices by conducting valid clinical trials. However, the study design and statistical analysis for these clinical trials are complicated. While these trials are designed to provide real-time intraoperative diagnosis of cancerous tissue, they also have clear therapeutic objectives to lower the reoperation rate of breast cancer surgery. This research article introduces the new concept of neutral diagnosis (ND), and the ND clinical trial design as an innovative study design to evaluate the effectiveness and safety of diagnostic devices with direct therapeutic purposes. A joint modeling approach is adopted to make inferences on the effectiveness and safety of these devices for non-neutral diagnosis (non-ND) clinical trials. Simulation studies were conducted to show the efficiency of the ND trials and strength of the joint modeling approach in the non-ND clinical trials. An example on a diagnostic medical device that provides real-time, intraoperative diagnosis of breast cancer tumor tissues during breast cancer surgeries is comprehensively discussed and analyzed

One-Step or Two-Step Acid/Alkaline Pretreatments to Improve Enzymatic Hydrolysis and Sugar Recovery from Arundo Donax L.

Energy crops are not easily converted by microorganisms because of their recalcitrance. This necessitates a pretreatment to improve their biodigestibility. The effects of different pretreatments, as well as their combination on the enzymatic digestibility of Arundo donax L. were systematically investigated to evaluate its potential for bioconversion. Dilute alkaline pretreatment (ALP) using 1.2% NaOH at 120 °C for 30 min resulted in the highest reducing sugar yield in the enzymatic hydrolysis process because of its strong delignification and morphological modification, while ferric chloride pretreatment (FP) was effective in removing hemicellulose and recovering soluble sugars in the pretreatment stage. Furthermore, an efficient two-step ferric chloride-alkaline pretreatment (FALP) was successfully developed. In the first FP step, easily degradable cellulosic components, especially hemicellulose, were dissolved and then effectively recovered as soluble sugars. Subsequently, the FP sample was further treated in the second ALP step to remove lignin to enhance the enzymatic hydrolysis of the hardly degradable cellulose. As a result, the integrated two-step process obtained the highest total sugar yield of 420.4 mg/g raw stalk in the whole pretreatment and enzymatic hydrolysis process; hence, the process is a valuable candidate for biofuel production

The impact of surgical volume on hospital ranking using the standardized infection ratio

Abstract The Centers for Medicare and Medicaid Services require hospitals to report on quality metrics which are used to financially penalize those that perform in the lowest quartile. Surgical site infections (SSIs) are a critical component of the quality metrics that target healthcare-associated infections. However, the accuracy of such hospital profiling is highly affected by small surgical volumes which lead to a large amount of uncertainty in estimating standardized hospital-specific infection rates. Currently, hospitals with less than one expected SSI are excluded from rankings, but the effectiveness of this exclusion criterion is unknown. Tools that can quantify the classification accuracy and can determine the minimal surgical volume required for a desired level of accuracy are lacking. We investigate the effect of surgical volume on the accuracy of identifying poorly performing hospitals based on the standardized infection ratio and develop simulation-based algorithms for quantifying the classification accuracy. We apply our proposed method to data from HCA Healthcare (2014–2016) on SSIs in colon surgery patients. We estimate that for a procedure like colon surgery with an overall SSI rate of 3%, to rank hospitals in the HCA colon SSI dataset, hospitals that perform less than 200 procedures have a greater than 10% chance of being incorrectly assigned to the worst performing quartile. Minimum surgical volumes and predicted events criteria are required to make evaluating hospitals reliable, and these criteria vary by overall prevalence and between-hospital variability

The impact of surgical volume on hospital ranking using the standardized infection ratio.

The Centers for Medicare and Medicaid Services require hospitals to report on quality metrics which are used to financially penalize those that perform in the lowest quartile. Surgical site infections (SSIs) are a critical component of the quality metrics that target healthcare-associated infections. However, the accuracy of such hospital profiling is highly affected by small surgical volumes which lead to a large amount of uncertainty in estimating standardized hospital-specific infection rates. Currently, hospitals with less than one expected SSI are excluded from rankings, but the effectiveness of this exclusion criterion is unknown. Tools that can quantify the classification accuracy and can determine the minimal surgical volume required for a desired level of accuracy are lacking. We investigate the effect of surgical volume on the accuracy of identifying poorly performing hospitals based on the standardized infection ratio and develop simulation-based algorithms for quantifying the classification accuracy. We apply our proposed method to data from HCA Healthcare (2014-2016) on SSIs in colon surgery patients. We estimate that for a procedure like colon surgery with an overall SSI rate of 3%, to rank hospitals in the HCA colon SSI dataset, hospitals that perform less than 200 procedures have a greater than 10% chance of being incorrectly assigned to the worst performing quartile. Minimum surgical volumes and predicted events criteria are required to make evaluating hospitals reliable, and these criteria vary by overall prevalence and between-hospital variability

Enhanced Enzymatic Hydrolysis of <i>Pennisetum alopecuroides</i> by Dilute Acid, Alkaline and Ferric Chloride Pretreatments

In this study, effects of different pretreatment methods on the enzymatic digestibility of Pennisetum alopecuroides, a ubiquitous wild grass in China, were investigated to evaluate its potential as a feedstock for biofuel production. The stalk samples were separately pretreated with H2SO4, NaOH and FeCl3 solutions of different concentrations at 120 &#176;C for 30 min, after which enzymatic hydrolysis was conducted to measure the digestibility of pretreated samples. Results demonstrated that different pretreatments were effective at removing hemicellulose, among which ferric chloride pretreatment (FCP) gave the highest soluble sugar recovery (200.2 mg/g raw stalk) from the pretreatment stage. In comparison with FCP and dilute acid pretreatment (DAP), dilute alkaline pretreatment (DALP) induced much higher delignification and stronger morphological changes of the biomass, making it more accessible to hydrolysis enzymes. As a result, DALP using 1.2% NaOH showed the highest total soluble sugar yield through the whole process from pretreatment to enzymatic hydrolysis (508.5 mg/g raw stalk). The present work indicates that DALP and FCP have the potential to enhance the effective bioconversion of lignocellulosic biomass like P. alopecuroides, hence making this material a valuable and promising energy plant