Browsing by Author "Zhang, Ying"
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Item A reference-free R-learner for treatment recommendation(Sage, 2023) Zhou, Junyi; Zhang, Ying; Tu, Wanzhu; Biostatistics and Health Data Science, School of MedicineAssigning optimal treatments to individual patients based on their characteristics is the ultimate goal of precision medicine. Deriving evidence-based recommendations from observational data while considering the causal treatment effects and patient heterogeneity is a challenging task, especially in situations of multiple treatment options. Herein, we propose a reference-free R-learner based on a simplex algorithm for treatment recommendation. We showed through extensive simulation that the proposed method produced accurate recommendations that corresponded to optimal treatment outcomes, regardless of the reference group. We used the method to analyze data from the Systolic Blood Pressure Intervention Trial (SPRINT) and achieved recommendations consistent with the current clinical guidelines.Item A spline-based nonparametric analysis for interval-censored bivariate survival data(Institute of Statistical Science, 2022) Wu, Yuan; Zhang, Ying; Zhou, Junyi; Biostatistics, School of Public HealthIn this manuscript we propose a spline-based sieve nonparametric maximum likelihood estimation method for joint distribution function with bivariate interval-censored data. We study the asymptotic behavior of the proposed estimator by proving the consistency and deriving the rate of convergence. Based on the sieve estimate of the joint distribution, we also develop an efficient nonparametric test for making inference about the dependence between two interval-censored event times and establish its asymptotic normality. We conduct simulation studies to examine the finite sample performance of the proposed methodology. Finally we apply the method to assess the association between two subtypes of mild cognitive impairment (MCI): amnestic MCI and non-amnestic MCI, for Huntington disease (HD) using data from a 12-year observational cohort study on premanifest HD individuals, PREDICT-HD.Item Advances in Therapeutic L-Nucleosides and L-Nucleic Acids with Unusual Handedness(MDPI, 2021-12-24) Dantsu, Yuliya; Zhang, Ying; Zhang, Wen; Biochemistry and Molecular Biology, School of MedicineNucleic-acid-based small molecule and oligonucleotide therapies are attractive topics due to their potential for effective target of disease-related modules and specific control of disease gene expression. As the non-naturally occurring biomolecules, modified DNA/RNA nucleoside and oligonucleotide analogues composed of L-(deoxy)riboses, have been designed and applied as innovative therapeutics with superior plasma stability, weakened cytotoxicity, and inexistent immunogenicity. Although all the chiral centers in the backbone are mirror converted from the natural D-nucleic acids, L-nucleic acids are equipped with the same nucleobases (A, G, C and U or T), which are critical to maintain the programmability and form adaptable tertiary structures for target binding. The types of L-nucleic acid drugs are increasingly varied, from chemically modified nucleoside analogues that interact with pathogenic polymerases to nanoparticles containing hundreds of repeating L-nucleotides that circulate durably in vivo. This article mainly reviews three different aspects of L-nucleic acid therapies, including pharmacological L-nucleosides, Spiegelmers as specific target-binding aptamers, and L-nanostructures as effective drug-delivery devices.Item Analysis of longitudinal censored semicontinuous data with application to the study of executive dysfunction: the Towers Task(Sage, 2017) Lourens, Spencer; Zhang, Ying; Long, Jeffrey D.; Paulsen, Jane S.; Biostatistics and Health Data Science, School of MedicineExecutive dysfunction is a deficiency in skills of planning and problem solving that characterizes many neuropsychiatric disorders. The Towers Task is a commonly used measure of planning and problem solving for assessing executive function. Towers Task data are usually zero-inflated and right-censored, and ignoring these features can result in biased inference for the disease characterization of executive dysfunction. In this manuscript, a mixed-effects model for longitudinal censored semicontinuous data is developed for analyzing longitudinal Towers Task data from the PREDICT-HD study. The model is contrasted with current practice and implications for general use are discussed.Item Assessment of the Protocol-Guided Rapid Evaluation of Veterans Experiencing New Transient Neurological Symptoms (PREVENT) Program for Improving Quality of Care for Transient Ischemic Attack: A Nonrandomized Cluster Trial(American Medical Association, 2020-09-08) Bravata, Dawn M.; Myers, Laura J.; Perkins, Anthony J.; Zhang, Ying; Miech, Edward J.; Rattray, Nicholas A.; Penney, Lauren S.; Levine, Deborah; Sico, Jason J.; Cheng, Eric M.; Damush, Teresa M.; Medicine, School of MedicineImportance Patients with transient ischemic attack (TIA) are at high risk of recurrent vascular events. Timely management can reduce that risk by 70%; however, gaps in TIA quality of care exist. Objective To assess the performance of the Protocol-Guided Rapid Evaluation of Veterans Experiencing New Transient Neurological Symptoms (PREVENT) intervention to improve TIA quality of care. Design, Setting, and Participants This nonrandomized cluster trial with matched controls evaluated a multicomponent intervention to improve TIA quality of care at 6 diverse medical centers in 6 geographically diverse states in the US and assessed change over time in quality of care among 36 matched control sites (6 control sites matched to each PREVENT site on TIA patient volume, facility complexity, and quality of care). The study period (defined as the data period) started on August 21, 2015, and extended to May 12, 2019, including 1-year baseline and active implementation periods for each site. The intervention targeted clinical teams caring for patients with TIA. Intervention The quality improvement (QI) intervention included the following 5 components: clinical programs, data feedback, professional education, electronic health record tools, and QI support. Main Outcomes and Measures The primary outcome was the without-fail rate, which was calculated as the proportion of veterans with TIA at a specific facility who received all 7 guideline-recommended processes of care for which they were eligible (ie, anticoagulation for atrial fibrillation, antithrombotic use, brain imaging, carotid artery imaging, high- or moderate-potency statin therapy, hypertension control, and neurological consultation). Generalized mixed-effects models with multilevel hierarchical random effects were constructed to evaluate the intervention associations with the change in the mean without-fail rate from the 1-year baseline period to the 1-year intervention period. Results Six facilities implemented the PREVENT QI intervention, and 36 facilities were identified as matched control sites. The mean (SD) age of patients at baseline was 69.85 (11.19) years at PREVENT sites and 71.66 (11.29) years at matched control sites. Most patients were male (95.1% [154 of 162] at PREVENT sites and 94.6% [920 of 973] at matched control sites at baseline). Among the PREVENT sites, the mean without-fail rate improved substantially from 36.7% (58 of 158 patients) at baseline to 54.0% (95 of 176 patients) during a 1-year implementation period (adjusted odds ratio, 2.10; 95% CI, 1.27-3.48; P = .004). Comparing the change in quality at the PREVENT sites with the matched control sites, the improvement in the mean without-fail rate was greater at the PREVENT sites than at the matched control sites (36.7% [58 of 158 patients] to 54.0% [95 of 176 patients] [17.3% absolute improvement] vs 38.6% [345 of 893 patients] to 41.8% [363 of 869 patients] [3.2% absolute improvement], respectively; absolute difference, 14%; P = .008). Conclusions and Relevance The implementation of this multifaceted program was associated with improved TIA quality of care across the participating sites. The PREVENT QI program is an example of a health care system using QI strategies to improve performance, and may serve as a model for other health systems seeking to provide better care. Trial Registration ClinicalTrials.gov Identifier: NCT02769338 Go to: Introduction Approximately 8500 veterans with transient ischemic attack (TIA) or ischemic stroke are cared for in Department of Veterans Affairs (VA) emergency departments (EDs) or inpatient wards annually in the United States.1 Patients with TIA generally present with transient neurological symptoms of a presumed ischemic cause.2 Patients with TIA are at a high risk of recurrent vascular events3,4,5; however, delivery of timely TIA care can reduce that risk by up to 70%.6,7,8,9 Despite the known benefits of timely TIA care, gaps in TIA quality of care exist in both private-sector US hospitals10 and VA facilities.11,12 In a learning health care system, “clinical informatics, incentives, and culture are aligned to promote continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience.”13(p136) Within a learning health care system, health care teams respond to quality problems by using quality improvement (QI) strategies and systems redesign approaches to improve performance, depending on the complexity and scope of the problem.14 The objective of the Protocol-Guided Rapid Evaluation of Veterans Experiencing New Transient Neurological Symptoms (PREVENT) trial was to evaluate a multicomponent QI intervention to improve the quality of TIA care.15 The PREVENT intervention was designed to align with the learning health care system model.13,15Item Association Between Antithrombotic Medication Use After Bioprosthetic Aortic Valve Replacement and Outcomes in the Veterans Health Administration System(American Medical Association (AMA), 2018-12-26) Bravata, Dawn M.; Coffing, Jessica M.; Kansagara, Devan; Myers, Jennifer; Murphy, Lauren; Homoya, Barbara J.; Perkins, Anthony J.; Snow, Kathryn; Quin, Jacquelyn A.; Zhang, Ying; Myers, Laura J.; Medicine, School of MedicineIMPORTANCE: The recommendations about antithrombotic medication use after bioprosthetic aortic valve replacement (bAVR) vary. OBJECTIVES: To describe the post-bAVR antithrombotic medication practice across the Veterans Health Administration (VHA) and to assess the association between antithrombotic strategies and post-bAVR outcomes. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study. Multivariable modeling with propensity scores was conducted to adjust for differences in patient characteristics across the 3 most common antithrombotic medication strategies (aspirin plus warfarin sodium, aspirin only, and dual antiplatelets). Text mining of notes was used to identify the patients with bAVR (fiscal years 2005-2015). MAIN OUTCOMES AND MEASURES: This study used VHA and non-VHA outpatient pharmacy data and text notes to classify the following antithrombotic medications prescribed within 1 week after discharge from the bAVR hospitalization: aspirin plus warfarin, aspirin only, dual antiplatelets, no antithrombotics, other only, and warfarin only. The 90-day outcomes included all-cause mortality, thromboembolism risk, and bleeding events. Outcomes were identified using primary diagnosis codes from emergency department visits or hospital admissions. RESULTS: The cohort included 9060 veterans with bAVR at 47 facilities (mean [SD] age, 69.3 [8.8] years; 98.6% male). The number of bAVR procedures per year increased from 610 in fiscal year 2005 to 1072 in fiscal year 2015. The most commonly prescribed antithrombotic strategy was aspirin only (4240 [46.8%]), followed by aspirin plus warfarin (1638 [18.1%]), no antithrombotics (1451 [16.0%]), dual antiplatelets (1010 [11.1%]), warfarin only (439 [4.8%]), and other only (282 [3.1%]). Facility variation in antithrombotic prescription patterns was observed. During the 90-day post-bAVR period, adverse events were uncommon, including all-cause mortality in 127 (1.4%), thromboembolism risk in 142 (1.6%), and bleeding events in 149 (1.6%). No differences in 90-day mortality or thromboembolism were identified across the 3 antithrombotic medication groups in either the unadjusted or adjusted models. Patients receiving the combination of aspirin plus warfarin had higher odds of bleeding than patients receiving aspirin only in the unadjusted analysis (odds ratio, 2.58; 95% CI, 1.71-3.89) and after full risk adjustment (adjusted odds ratio, 1.92; 95% CI, 1.17-3.14). CONCLUSIONS AND RELEVANCE: These data demonstrate that bAVR procedures are increasingly being performed in VHA facilities and that aspirin only was the most commonly used antithrombotic medication strategy after bAVR. The risk-adjusted results suggest that the combination of aspirin plus warfarin does not improve either all-cause mortality or thromboembolism risk but increases the risk of bleeding events compared with aspirin only.Item Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic(AMA, 2021) Bravata, Dawn M.; Perkins, Anthony J.; Myers, Laura J.; Arling, Greg; Zhang, Ying; Zillich, Alan J.; Reese, Lindsey; Dysangco, Andrew; Agarwal, Rajiv; Myers, Jennifer; Austin, Charles; Sexson, Ali; Leonard, Samuel J.; Dev, Sharmistha; Keyhani, Salomeh; Medicine, School of MedicineImportance Although strain on hospital capacity has been associated with increased mortality in nonpandemic settings, studies are needed to examine the association between coronavirus disease 2019 (COVID-19) critical care capacity and mortality. Objective To examine whether COVID-19 mortality was associated with COVID-19 intensive care unit (ICU) strain. Design, Setting, and Participants This cohort study was conducted among veterans with COVID-19, as confirmed by polymerase chain reaction or antigen testing in the laboratory from March through August 2020, cared for at any Department of Veterans Affairs (VA) hospital with 10 or more patients with COVID-19 in the ICU. The follow-up period was through November 2020. Data were analyzed from March to November 2020. Exposures Receiving treatment for COVID-19 in the ICU during a period of increased COVID-19 ICU load, with load defined as mean number of patients with COVID-19 in the ICU during the patient’s hospital stay divided by the number of ICU beds at that facility, or increased COVID-19 ICU demand, with demand defined as mean number of patients with COVID-19 in the ICU during the patient’s stay divided by the maximum number of patients with COVID-19 in the ICU. Main Outcomes and Measures All-cause mortality was recorded through 30 days after discharge from the hospital. Results Among 8516 patients with COVID-19 admitted to 88 VA hospitals, 8014 (94.1%) were men and mean (SD) age was 67.9 (14.2) years. Mortality varied over time, with 218 of 954 patients (22.9%) dying in March, 399 of 1594 patients (25.0%) dying in April, 143 of 920 patients (15.5%) dying in May, 179 of 1314 patients (13.6%) dying in June, 297 of 2373 patients (12.5%) dying in July, and 174 of 1361 (12.8%) patients dying in August (P < .001). Patients with COVID-19 who were treated in the ICU during periods of increased COVID-19 ICU demand had increased risk of mortality compared with patients treated during periods of low COVID-19 ICU demand (ie, demand of ≤25%); the adjusted hazard ratio for all-cause mortality was 0.99 (95% CI, 0.81-1.22; P = .93) for patients treated when COVID-19 ICU demand was more than 25% to 50%, 1.19 (95% CI, 0.95-1.48; P = .13) when COVID-19 ICU demand was more than 50% to 75%, and 1.94 (95% CI, 1.46-2.59; P < .001) when COVID-19 ICU demand was more than 75% to 100%. No association between COVID-19 ICU demand and mortality was observed for patients with COVID-19 not in the ICU. The association between COVID-19 ICU load and mortality was not consistent over time (ie, early vs late in the pandemic). Conclusions and Relevance This cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased COVID-19 ICU mortality. Tracking COVID-19 ICU demand may be useful to hospital administrators and health officials as they coordinate COVID-19 admissions across hospitals to optimize outcomes for patients with this illness.Item Asymptotic normality of nonparametric M-estimators with applications to hypothesis testing for panel count data(2017) Zhao, Xingqiu; Zhang, Ying; Biostatistics, School of Public HealthIn semiparametric and nonparametric statistical inference, the asymptotic normality of estimators has been widely established when they are \sqrt{n} -consistent. In many applications, nonparametric estimators are not able to achieve this rate. We have a result on the asymptotic normality of nonparametric M - estimators that can be used if the rate of convergence of an estimator is n^{-\dfrac{1}{2}} or slower. We apply this to study the asymptotic distribution of sieve estimators of functionals of a mean function from a counting process, and develop nonparametric tests for the problem of treatment comparison with panel count data. The test statistics are constructed with spline likelihood estimators instead of nonparametric likelihood estimators. The new tests have a more general and simpler structure and are easy to implement. Simulation studies show that the proposed tests perform well even for small sample sizes. We find that a new test is always powerful for all the situations considered and is thus robust. For illustration, a data analysis example is provided.Item A clinical trial method to show delay of onset in Huntington disease(Wiley, 2019-02) Paulsen, Jane S.; Lourens, Spencer; Kieburtz, Karl; Zhang, Ying; Biostatistics, School of Public HealthBackground: Disease-modifying clinical trials in persons without symptoms are often limited in methods to assess the impact associated with experimental therapeutics. This study suggests sample enrichment approaches to facilitate preventive trials to delay disease onset in individuals with the dominant gene for Huntington disease. Methods: Using published onset prediction indexes, we conducted the receiver operating curve analysis for diagnosis within a 3-year clinical trial time frame. We determined optimal cut points on the indexes for participant recruitment and then conducted sample size and power calculations to detect varying effect sizes for treatment efficacy in reducing 3-year rates of disease onset (or diagnosis). Results: Area under the curve for 3 onset prediction indexes all demonstrated excellent value in sample enrichment methodology, with the best-performing index being the multivariate risk score (MRS). Conclusions: This study showed that conducting an intervention trial in premanifest and prodromal individuals with the gene expansion for Huntington disease is highly feasible using sample enrichment recruitment methods. Ongoing natural history studies are highly likely to indicate additional markers of disease prior to diagnosis. Statistical modeling of identified markers can facilitate participant enrichment to increase the likelihood of detecting a difference between treatment arms in a cost-effective and efficient manner. Such variations may expedite translation of emerging therapies to persons in an earlier phase of the disease.Item Current Perspectives of Neuroendocrine Regulation in Liver Fibrosis(MDPI, 2022-11-26) Li, Bowen; Wang, Hui; Zhang, Yudian; Liu, Ying; Zhou, Tiejun; Zhou, Bingru; Zhang, Ying; Chen, Rong; Xing, Juan; He, Longfei; Salinas, Jennifer Mata; Koyama, Sachiko; Meng, Fanyin; Wan, Ying; Medicine, School of MedicineLiver fibrosis is a complicated process that involves different cell types and pathological factors. The excessive accumulation of extracellular matrix (ECM) and the formation of fibrotic scar disrupt the tissue homeostasis of the liver, eventually leading to cirrhosis and even liver failure. Myofibroblasts derived from hepatic stellate cells (HSCs) contribute to the development of liver fibrosis by producing ECM in the area of injuries. It has been reported that the secretion of the neuroendocrine hormone in chronic liver injury is different from a healthy liver. Activated HSCs and cholangiocytes express specific receptors in response to these neuropeptides released from the neuroendocrine system and other neuroendocrine cells. Neuroendocrine hormones and their receptors form a complicated network that regulates hepatic inflammation, which controls the progression of liver fibrosis. This review summarizes neuroendocrine regulation in liver fibrosis from three aspects. The first part describes the mechanisms of liver fibrosis. The second part presents the neuroendocrine sources and neuroendocrine compartments in the liver. The third section discusses the effects of various neuroendocrine factors, such as substance P (SP), melatonin, as well as α-calcitonin gene-related peptide (α-CGRP), on liver fibrosis and the potential therapeutic interventions for liver fibrosis.