Browsing by Author "Zhao, Yi"
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Item ADHD-related sex differences in emotional symptoms across development(Springer, 2024) De Ronda, Alyssa C.; Rice, Laura; Zhao, Yi; Rosch, Keri S.; Mostofsky, Stewart H.; Seymour, Karen E.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthTo investigate developmental changes in emotion dysregulation (ED) and associated symptoms of emotional lability, irritability, anxiety, and depression, among girls and boys with and without ADHD from childhood through adolescence. Data were collected from a sample of 8-18-year-old children with (n = 264; 76 girls) and without (n = 153; 56 girls) ADHD, with multiple time-points from a subsample of participants (n = 121). Parents and youth completed rating scales assessing child ED, emotional lability, irritability, anxiety, and depression. Mixed effects models were employed to examine effects and interactions of diagnosis, sex [biological sex assigned at birth], age among boys and girls with and without ADHD. Mixed effects analyses showed sexually dimorphic developmental patterns between boys and girls, such that boys with ADHD showed a greater reduction in ED, irritability, and anxiety with age compared to girls with ADHD, whose symptom levels remained elevated relative to TD girls. Depressive symptoms were persistently elevated among girls with ADHD compared to boys with ADHD, whose symptoms decreased with age, relative to same-sex TD peers. While both boys and girls with ADHD showed higher levels of ED during childhood (compared to their sex-matched TD peers), mixed effects analyses revealed substantial sexually dimorphic patterns of emotional symptom change during adolescence: Boys with ADHD showed robust improvements in emotional symptoms from childhood to adolescence while girls with ADHD continued to show high and/or increased levels of ED, emotional lability, irritability, anxiety and depression.Item Antigen stasis and airway nitrosative stress in human primary ciliary dyskinesia(American Physiological Society, 2024) Gaston, Benjamin; Smith, Laura A.; Davis, Michael D.; Saunders, Jessica; Daniels, Ivana; Horani, Amjad; Brody, Steven L.; Giddings, Olivia; Zhao, Yi; Marozkina, Nadzeya; Pediatrics, School of MedicineNasal nitric oxide (nNO) is low in most patients with primary ciliary dyskinesia (PCD). Decreased ciliary motion could lead to antigen stasis, increasing oxidant production and NO oxidation in the airways. This could both decrease gas phase NO and increase nitrosative stress. We studied primary airway epithelial cells from healthy controls (HCs) and patients with PCD with several different genotypes. We measured antigen clearance in fenestrated membranes exposed apically to the fluorescently labeled antigen Dermatophagoides pteronyssinus (Derp1-f). We immunoblotted for 3-nitrotyrosine (3-NT) and for oxidative response enzymes. We measured headspace NO above primary airway cells without and with a PCD-causing genotype. We measured nNO and exhaled breath condensate (EBC) H2O2 in vivo. Apical Derp1-f was cleared from HC better than from PCD cells. DUOX1 expression was lower in HC than in PCD cells at baseline and after 24-h Derp1-f exposure. HC cells had less 3-NT and NO3- than PCD cells. However, NO consumption by HC cells was less than that by PCD cells; NO loss was prevented by superoxide dismutase (SOD) and by apocynin. nNO was higher in HCs than in patients with PCD. EBC H2O2 was lower in HC than in patients with PCD. The PCD airway epithelium does not optimally clear antigens and is subject to oxidative and nitrosative stress. Oxidation associated with antigen stasis could represent a therapeutic target in PCD, one with convenient monitoring biomarkers. NEW & NOTEWORTHY: The PCD airway epithelium does not optimally clear antigens, and antigen exposure can lead to NO oxidation and nitrosative stress. Oxidation caused by antigen stasis could represent a therapeutic target in PCD, and there are convenient monitoring biomarkers.Item Applications of Time to Event Analysis in Clinical Data(2021-12) Xu, Chenjia; Gao, Sujuan; Liu, Hao; Zang, Yong; Zhang, Jianjun; Zhao, YiSurvival analysis has broad applications in diverse research areas. In this dissertation, we consider an innovative application of survival analysis approach to phase I dose-finding design and the modeling of multivariate survival data. In the first part of the dissertation, we apply time to event analysis in an innovative dose-finding design. To account for the unique feature of a new class of oncology drugs, T-cell engagers, we propose a phase I dose-finding method incorporating systematic intra-subject dose escalation. We utilize survival analysis approach to analyze intra-subject dose-escalation data and to identify the maximum tolerated dose. We evaluate the operating characteristics of the proposed design through simulation studies and compare it to existing methodologies. The second part of the dissertation focuses on multivariate survival data with semi-competing risks. Time-to-event data from the same subject are often correlated. In addition, semi-competing risks are sometimes present with correlated events when a terminal event can censor other non-terminal events but not vice versa. We use a semiparametric frailty model to account for the dependence between correlated survival events and semi-competing risks and adopt penalized partial likelihood (PPL) approach for parameter estimation. In addition, we investigate methods for variable selection in semi-parametric frailty models and propose a double penalized partial likelihood (DPPL) procedure for variable selection of fixed effects in frailty models. We consider two penalty functions, least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) penalty. The proposed methods are evaluated in simulation studies and illustrated using data from Indianapolis-Ibadan Dementia Project.Item Association of Plasma Tau With Mortality and Long-term Neurocognitive Impairment in Survivors of Pediatric Cerebral Malaria and Severe Malarial Anemia(American Medical Association, 2021-12) Datta, Dibyadyuti; Bangirana, Paul; Opoka, Robert O.; Conroy, Andrea L.; Co, Katrina; Bond, Caitlin; Zhao, Yi; Kawata, Keisuke; Saykin, Andrew J.; John, Chandy C.; Biostatistics and Health Data Science, School of MedicineImportance: Cerebral malaria (CM) and severe malarial anemia (SMA) are associated with persistent neurocognitive impairment (NCI) among children in Africa. Identifying blood biomarkers of acute brain injury that are associated with future NCI could allow early interventions to prevent or reduce NCI in survivors of severe malaria. Objective: To investigate whether acutely elevated tau levels are associated with future NCI in children after CM or SMA. Design, setting, and participants: This prospective cohort study was conducted at Mulago National Referral Hospital in Kampala, Uganda, from March 2008 to October 2015. Children aged 1.5 to 12 years with CM (n = 182) or SMA (n = 162) as well as community children (CC; n = 123) were enrolled in the study. Data analysis was conducted from January 2020 to May 2021. Exposure: CM or SMA. Main outcomes and measures: Enrollment plasma tau levels were measured using single-molecule array detection technology. Overall cognition (primary) and attention and memory (secondary) z scores were measured at 1 week and 6, 12, and 24 months after discharge using tools validated in Ugandan children younger than 5 years or 5 years and older. Results: A total of 467 children were enrolled. In the CM group, 75 (41%) were girls, and the mean (SD) age was 4.02 (1.92) years. In the SMA group, 59 (36%) were girls, and the mean (SD) age was 3.45 (1.60) years. In the CC group, 65 (53%) were girls, and the mean (SD) age was 3.94 (1.92) years. Elevated plasma tau levels (>95th percentile in CC group; >6.43 pg/mL) were observed in 100 children (55%) with CM and 69 children (43%) with SMA (P < .001). In children with CM who were younger than 5 years, elevated plasma tau levels were associated with increased mortality (odds ratio [OR], 3.06; 95% CI, 1.01-9.26; P = .048). In children with CM who were younger than 5 years at both CM episode and follow-up neurocognitive testing, plasma tau levels (log10 transformed) were associated with worse overall cognition scores over 24-month follow-up (β = -0.80; 95% CI, -1.32 to -0.27; P = .003). In children with CM who were younger than 5 years at CM episode and 5 years or older at follow-up neurocognitive testing, plasma tau was associated with worse scores in attention (β = -1.08; 95% CI, -1.79 to -0.38; P = .003) and working memory (β = -1.39; 95% CI, -2.18 to -0.60; P = .001). Conclusions and relevance: In this study, plasma tau, a marker of injury to neuronal axons, was elevated in children with CM or SMA and was associated with mortality and persistent NCI in children with CM younger than 5 years.Item Automatic comprehensive radiological reports for clinical acute stroke MRIs(Springer Nature, 2023-07-10) Liu, Chin-Fu; Zhao, Yi; Yedavalli, Vivek; Leigh, Richard; Falcao, Vito; STIR and VISTA Imaging investigators; Miller, Michael I.; Hillis, Argye E.; Faria, Andreia V.; Biostatistics and Health Data Science, School of MedicineBackground: Although artificial intelligence systems that diagnosis among different conditions from medical images are long term aims, specific goals for automation of human-labor, time-consuming tasks are not only feasible but equally important. Acute conditions that require quantitative metrics, such as acute ischemic strokes, can greatly benefit by the consistency, objectiveness, and accessibility of automated radiological reports. Methods: We used 1,878 annotated brain MRIs to generate a fully automated system that outputs radiological reports in addition to the infarct volume, 3D digital infarct mask, and the feature vector of anatomical regions affected by the acute infarct. This system is associated to a deep-learning algorithm for segmentation of the ischemic core and to parcellation schemes defining arterial territories and classically-identified anatomical brain structures. Results: Here we show that the performance of our system to generate radiological reports was comparable to that of an expert evaluator. The weight of the components of the feature vectors that supported the prediction of the reports, as well as the prediction probabilities are outputted, making the pre-trained models behind our system interpretable. The system is publicly available, runs in real time, in local computers, with minimal computational requirements, and it is readily useful for non-expert users. It supports large-scale processing of new and legacy data, enabling clinical and translational research. Conclusion: The generation of reports indicates that our fully automated system is able to extract quantitative, objective, structured, and personalized information from stroke MRIs.Item B-Value and Empirical Equivalence Bound: A New Procedure of Hypothesis Testing(Wiley, 2022) Zhao, Yi; Caffo, Brian S.; Ewen, Joshua B.; Biostatistics and Health Data Science, School of MedicineIn this study, we propose a two-stage procedure for hypothesis testing, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using an introduced Empirical Equivalence Bound. In 2016, the American Statistical Association released a policy statement on P-values to clarify the proper use and interpretation in response to the criticism of reproducibility and replicability in scientific findings. A recent solution to improve reproducibility and transparency in statistical hypothesis testing is to integrate P-values (or confidence intervals) with practical or scientific significance. Similar ideas have been proposed via the equivalence test, where the goal is to infer equality under a presumption (null) of inequality of parameters. However, the definition of scientific significance/equivalence can sometimes be ill-justified and subjective. To circumvent this drawback, we introduce the B-value and the Empirical Equivalence Bound, which are both estimated from the data. Performing a second-stage equivalence test, our procedure offers an opportunity to improve the reproducibility of findings across studies.Item Bayesian Adaptive Designs for Early Phase Clinical Trials(2023-07) Guo, Jiaying; Zang, Yong; Han, Jiali; Zhao, Yi; Ren, JieDelayed toxicity outcomes are common in phase I clinical trials, especially in oncology studies. It causes logistic difficulty, wastes resources, and prolongs the trial duration. We propose the time-to-event 3+3 (T-3+3) design to solve the delayed outcome issue for the 3+3 design. We convert the dose decision rules of the 3+3 design into a series of events. A transparent yet efficient Bayesian probability model is applied to calculate the event happening probabilities in the presence of delayed outcomes, which incorporates the informative pending patients' remaining follow-up time into consideration. The T-3+3 design only models the information for the pending patients and seamlessly reduces to the conventional 3+3 design in the absence of delayed outcomes. We further extend the proposed method to interval 3+3 (i3+3) design, an algorithm-based phase I dose-finding design which is based on simple but more comprehensive rules that account for the variabilities in the observed data. Similarly, the dose escalation/deescalation decision is recommended by comparing the event happening probabilities which are calculated by considering the ratio between the averaged follow-up time for at-risk patients and the total assessment window. We evaluate the operating characteristics of the proposed designs through simulation studies and compare them to existing methods. The umbrella trial is a clinical trial strategy that accommodates the paradigm shift towards personalized medicine, which evaluates multiple investigational drugs in different subgroups of patients with the same disease. A Bayesian adaptive umbrella trial design is proposed to select effective targeted agents for different biomarker-based subgroups of patients. To facilitate treatment evaluation, the design uses a mixture regression model that jointly models short-term and long-term response outcomes. In addition, a data-driven latent class model is employed to adaptively combine subgroups into induced latent classes based on overall data heterogeneities, which improves the statistical power of the umbrella trial. To enhance individual ethics, the design includes a response-adaptive randomization scheme with early stopping rules for futility and superiority. Bayesian posterior probabilities are used to make these decisions. Simulation studies demonstrate that the proposed design outperforms two conventional designs across a range of practical treatment-outcome scenarios.Item Bayesian mixed model inference for genetic association under related samples with brain network phenotype(Oxford University Press, 2024) Tian, Xinyuan; Wang, Yiting; Wang, Selena; Zhao, Yi; Zhao, Yize; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthGenetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.Item Benefits of Airway Androgen Receptor Expression in Human Asthma(American Thoracic Society, 2021) Zein, Joe G.; McManus, Jeffrey M.; Sharifi, Nima; Erzurum, Serpil C.; Marozkina, Nadzeya; Lahm, Timothy; Giddings, Olivia; Davis, Michael D.; DeBoer, Mark D.; Comhair, Suzy A.; Bazeley, Peter; Kim, Hyun Jo; Busse, William; Calhoun, William; Castro, Mario; Chung, Kian Fan; Fahy, John V.; Israel, Elliot; Jarjour, Nizar N.; Levy, Bruce D.; Mauger, David T.; Moore, Wendy C.; Ortega, Victor E.; Peters, Michael; Bleecker, Eugene R.; Meyers, Deborah A.; Zhao, Yi; Wenzel, Sally E.; Gaston, Benjamin; Biostatistics, School of Public HealthRationale: Androgens are potentially beneficial in asthma, but AR (androgen receptor) has not been studied in human airways. Objectives: To measure whether AR and its ligands are associated with human asthma outcomes. Methods: We compared the effects of AR expression on lung function, symptom scores, and fractional exhaled nitric oxide (FeNO) in adults enrolled in SARP (Severe Asthma Research Program). The impact of sex and of androgens on asthma outcomes was also evaluated in the SARP with validation studies in the Cleveland Clinic Health System and the NHANES (U.S. National Health and Nutrition Examination Survey).Measurements and Main Results: In SARP (n = 128), AR gene expression from bronchoscopic epithelial brushings was positively associated with both FEV1/FVC ratio (R2 = 0.135, P = 0.0002) and the total Asthma Quality of Life Questionnaire score (R2 = 0.056, P = 0.016) and was negatively associated with FeNO (R2 = 0.178, P = 9.8 × 10-6) and NOS2 (nitric oxide synthase gene) expression (R2 = 0.281, P = 1.2 × 10-10). In SARP (n = 1,659), the Cleveland Clinic Health System (n = 32,527), and the NHANES (n = 2,629), women had more asthma exacerbations and emergency department visits than men. The levels of the AR ligand precursor dehydroepiandrosterone sulfate correlated positively with the FEV1 in both women and men. Conclusions: Higher bronchial AR expression and higher androgen levels are associated with better lung function, fewer symptoms, and a lower FeNO in human asthma. The role of androgens should be considered in asthma management.Item Beyond Massive Univariate Tests: Covariance Regression Reveals Complex Patterns of Functional Connectivity Related to Attention-Deficit/Hyperactivity Disorder, Age, Sex, and Response Control(Elsevier, 2022) Zhao, Yi; Nebel, Mary Beth; Caffo, Brian S.; Mostofsky, Stewart H.; Rosch, Keri S.; Biostatistics and Health Data Science, School of MedicineBackground: Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study we apply a novel whole-matrix regression approach referred to as Covariate Assisted Principal (CAP) regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control. Methods: Participants included 8-12 year-old children with ADHD (n=115, 29 girls) and typically developing controls (n=102, 35 girls) who completed a resting-state fMRI scan and a go/no-go task (GNG). We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau). Results: The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models suggesting some specificity to the covariates of interest. Conclusions: These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.