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1. OU-CoViT: Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF Images

Author

Li, Yang, Deng, Jianing, Zhong, Chong, Yang, Danjuan, Li, Meiyan, Welsh, A. H., Liu, Aiyi, Zhou, Xingtao, Liu, Catherine C., and Fu, Bo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging and joint modeling of multiple discrete and continuous clinical scores presents a promising new paradigm for multi-task problems in Ophthalmology. The bi-channel framework that arises from the Ophthalmic phenomenon of ``interocular asymmetries'' of both eyes (OU) calls for new employment on the SOTA transformer-based models. However, the application of copula models for multiple mixed discrete-continuous labels on deep learning (DL) is challenging. Moreover, the application of advanced large transformer-based models to small medical datasets is challenging due to overfitting and computational resource constraints. To resolve these challenges, we propose OU-CoViT: a novel Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF images, which can i) incorporate conditional correlation information across multiple discrete and continuous labels within a deep learning framework (by deriving the closed form of a novel Copula Loss); ii) take OU inputs subject to both high correlation and interocular asymmetries using a bi-channel model with dual adaptation; and iii) enable the adaptation of large vision transformer (ViT) models to small medical datasets. Solid experiments demonstrate that OU-CoViT significantly improves prediction performance compared to single-channel baseline models with empirical loss. Furthermore, the novel architecture of OU-CoViT allows generalizability and extensions of our dual adaptation and Copula Loss to various ViT variants and large DL models on small medical datasets. Our approach opens up new possibilities for joint modeling of heterogeneous multi-channel input and mixed discrete-continuous clinical scores in medical practices and has the potential to advance AI-assisted clinical decision-making in various medical domains beyond Ophthalmology.

Published
2024

2. OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images

Author

Li, Yang, Huang, Qiuyi, Zhong, Chong, Yang, Danjuan, Li, Meiyan, Welsh, A. H., Liu, Aiyi, Fu, Bo, Liu, Catherien C., and Zhou, Xingtao

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging is potentially significant for ophthalmic outcomes. Current multidisciplinary research between ophthalmology and deep learning (DL) concentrates primarily on disease classification and diagnosis using single-eye images, largely ignoring joint modeling and prediction for Oculus Uterque (OU, both eyes). Inspired by the complex relationships between OU and the high correlation between the (continuous) outcome labels (Spherical Equivalent and Axial Length), we propose a framework of copula-enhanced adapter convolutional neural network (CNN) learning with OU UWF fundus images (OUCopula) for joint prediction of multiple clinical scores. We design a novel bi-channel multi-label CNN that can (1) take bi-channel image inputs subject to both high correlation and heterogeneity (by sharing the same backbone network and employing adapters to parameterize the channel-wise discrepancy), and (2) incorporate correlation information between continuous output labels (using a copula). Solid experiments show that OUCopula achieves satisfactory performance in myopia score prediction compared to backbone models. Moreover, OUCopula can far exceed the performance of models constructed for single-eye inputs. Importantly, our study also hints at the potential extension of the bi-channel model to a multi-channel paradigm and the generalizability of OUCopula across various backbone CNNs.

Published
2024

5. Low-rank Latent Matrix-factor Prediction Modeling for Generalized High-dimensional Matrix-variate Regression

Author

Zhang, Yuzhe, Zhang, Xu, Zhang, Hong, Liu, Aiyi, and Liu, Catherine

Subjects
Statistics - Applications
Abstract

Motivated by diagnosing the COVID-19 disease using 2D image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict responses that may come from an exponential distribution family, where covariates include high-dimensional matrix-variate biomarkers. A latent generalized matrix regression (LaGMaR) is formulated, where the latent predictor is a low-dimensional matrix factor score extracted from the low-rank signal of the matrix variate through a cutting-edge matrix factor model. Unlike the general spirit of penalizing vectorization plus the necessity of tuning parameters in the literature, instead, our prediction modeling in LaGMaR conducts dimension reduction that respects the geometry characteristic of intrinsic two-dimensional structure of the matrix covariate and thus avoids iteration. This greatly relieves the computation burden, and meanwhile maintains structural information so that the latent matrix factor feature can perfectly replace the intractable matrix-variate owing to high-dimensionality. The estimation procedure of LaGMaR is subtly derived by transforming the bilinear form matrix factor model onto a high-dimensional vector factor model, so that the method of principle components can be applied. We establish bilinear-form consistency of the estimated matrix coefficient of the latent predictor and consistency of prediction. The proposed approach can be implemented conveniently. Through simulation experiments, prediction capability of LaGMaR is shown to outperform existing penalized methods under diverse scenarios of generalized matrix regressions. Through the application to a real COVID-19 dataset, the proposed approach is shown to predict efficiently the COVID-19.

Published
2022

9. Distance-based regression analysis for measuring associations

Author

Shi, Yuke, Zhang, Wei, Liu, Aiyi, and Li, Qizhai

Subjects
Mathematics - Statistics Theory
Abstract

Distance-based regression model, as a nonparametric multivariate method, has been widely used to detect the association between variations in a distance or dissimilarity matrix for outcomes and predictor variables of interest in genetic association studies, genomic analyses, and many other research areas. Based on it, a pseudo-$F$ statistic which partitions the variation in distance matrices is often constructed to achieve the aim. To the best of our knowledge, the statistical properties of the pseudo-$F$ statistic has not yet been well established in the literature. To fill this gap, we study the asymptotic null distribution of the pseudo-$F$ statistic and show that it is asymptotically equivalent to a mixture of chi-squared random variables. Given that the pseudo-$F$ test statistic has unsatisfactory power when the correlations of the response variables are large, we propose a square-root $F$-type test statistic which replaces the similarity matrix with its square root. The asymptotic null distribution of the new test statistic and power of both tests are also investigated. Simulation studies are conducted to validate the asymptotic distributions of the tests and demonstrate that the proposed test has more robust power than the pseudo-$F$ test. Both test statistics are exemplified with a gene expression dataset for a prostate cancer pathway. Keywords: Asymptotic distribution, Chi-squared-type mixture, Nonparametric test, Pseudo-$F$ test, Similarity matrix., Comment: Acceptted by Journal of Systems Science & Complexity

Published
2021

12. The mediating role of rumination in the relation between self-compassion, posttraumatic stress disorder, and posttraumatic growth among adolescents after the Jiuzhaigou earthquake

Author

Liu, Aiyi, Wang, Wenchao, and Wu, Xinchun

Subjects
Sichuan, China, Earthquake, 2008 -- Environmental aspects -- Psychological aspects -- Health aspects, Teenagers -- Environmental aspects -- Psychological aspects -- Health aspects, Youth -- Environmental aspects -- Psychological aspects -- Health aspects, Post-traumatic stress disorder -- Risk factors -- Environmental aspects, Rumination (Psychology) -- Health aspects -- Environmental aspects, Psychology and mental health
Abstract

Previous studies indicated that self-compassion can alleviate posttraumatic stress disorder (PTSD) and promote posttraumatic growth (PTG). Rumination is a cognitive process in which individuals repeatedly think about traumatic events and their consequences, which includes invasive rumination (IR) and deliberate rumination (DR). IR is a process of thoughts over traumatic event that invades the cognitive world in an undesired state, whereas DR is an individual's conscious and repeated thinking on events. IR and DR have also been shown to have effects on PTSD and PTG. Studies have examined the relationship between self-compassion, IR, deliberate rumination DR, PTSD, and PTG, respectively, but only a few studies have examined their combined roles in PTSD and PTG among adolescents who have experienced an earthquake. In addition, recent studies have shown that the positive and negative components of self-compassion have different effects on post-traumatic stress responses, but few studies have explored the mechanisms of positive and negative self-compassion on PTSD and PTG in adolescents who experience earthquakes. This study examines the mediating roles of rumination in the relationship between self-compassion, PTSD and PTG among Chinese adolescents after the Jiuzhaigou earthquake. Ten months after the Jiuzhaigou earthquake, a questionnaire survey was conducted among 621 middle school students in Jiuzhaigou county. The participants were assessed by using the following systems: Trauma Exposure Questionnaire, Self-Compassion Scale, Event-Related Rumination Inventory, PTSD Checklist for DSM-5, and Posttraumatic Growth Inventory. When we controlled for the gender, age, and trauma exposure in the direct effect model, positive self-compassion (PSC) had a negative effect on PTSD and a positive effect on PTG. Moreover, negative self-compassion (NSC) had a positive effect on PTSD, but the effect on PTG was nonsignificant. In the indirect model, PSC had an indirect and positive effect on PTSD and PTG via deliberate rumination (DR). NSC had an indirect and positive effect on PTSD and PTG via invasive rumination (IR), whereas NSC also had an indirect and negative effect on PTSD and PTG, as well as via an indirect path from IR to DR on PTSD and PTG. Findings indicated that increased PSC may reduce PTSD and be beneficial for PTG. NSC may exacerbate PTSD. Rumination plays a significant mediating role between self-compassion, PTSD, and PTG., Author(s): Aiyi Liu [sup.1] , Wenchao Wang [sup.1] , Xinchun Wu [sup.1] Author Affiliations: (1) grid.20513.35, 0000 0004 1789 9964, Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center [...]

Published
2023
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14. Profile and Globe Tests of Mean Surfaces for Two-Sample Bivariate Functional Data

Author

Yang, Jin, Zhang, Tao, Liu, Chunling, Yuen, Kam Chuen, and Liu, Aiyi

Subjects
Statistics - Methodology
Abstract

Multivariate functional data has received considerable attention but testing for equality of mean surfaces and its profile has limited progress. The existing literature has tested equality of either mean curves of univariate functional samples directly, or mean surfaces of bivariate functional data samples but turn into functional curves comparison again. In this paper, we aim to develop both the profile and globe tests of mean surfaces for two-sample bivariate functional data. We present valid approaches of tests by employing the idea of pooled projection and by developing a novel profile functional principal component analysis tool. The proposed methodology enjoys the merit of readily interpretability and implementation. Under mild conditions, we derive the asymptotic behaviors of test statistics under null and alternative hypotheses. Simulations show that the proposed tests have a good control of the type I error by the size and can detect difference in mean surfaces and its profile effectively in terms of power in finite samples. Finally, we apply the testing procedures to two real data sets associated with the precipitation change affected jointly by time and locations in the Midwest of USA, and the trends in human mortality from European period life tables.

Published
2019

15. SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics.

Author

Song, Lei, Liu, Aiyi, Shi, Jianxin, Gejman, PV, Sanders, AR, Duan, J, Cloninger, CR, Svrakic, DM, Buccola, NG, Levinson, DF, Mowry, BJ, Freedman, R, Olincy, A, Amin, F, Black, DW, Silverman, JM, and Byerley, WF

Subjects
Mathematical Sciences, Biological Sciences, Statistics, Genetics, Human Genome, Prevention, Aetiology, 2.5 Research design and methodologies (aetiology), Good Health and Well Being, Genome-Wide Association Study, Genotype, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Schizophrenia, Molecular Genetics of Schizophrenia Consortium, Information and Computing Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

MOTIVATION:Polygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training datasets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods for approximating AUC of PRSs based on the summary level data of the validation dataset, which will greatly facilitate the development of PRS models for complex diseases. RESULTS:We develop statistical methods and an R package SummaryAUC for approximating the AUC and its variance of a PRS when only the summary level data of the validation dataset are available. SummaryAUC can be applied to PRSs with SNPs either genotyped or imputed in the validation dataset. We examined the performance of SummaryAUC using a large-scale GWAS of schizophrenia. SummaryAUC provides accurate approximations to AUCs and their variances. The bias of AUC is typically

Published
2019

16. A prospective study of artificially sweetened beverage intake and cardiometabolic health among women at high risk

Author

Hinkle, Stefanie N, Rawal, Shristi, Bjerregaard, Anne Ahrendt, Halldorsson, Thor I, Li, Mengying, Ley, Sylvia H, Wu, Jing, Zhu, Yeyi, Chen, Liwei, Liu, Aiyi, Grunnet, Louise Groth, Rahman, Mohammad L, Kampmann, Freja Bach, Mills, James L, Olsen, Sjurdur F, and Zhang, Cuilin

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Nutrition and Dietetics, Diabetes, Prevention, Clinical Research, Obesity, Nutrition, Reproductive health and childbirth, Metabolic and endocrine, Good Health and Well Being, Adult, Beverages, Body Mass Index, Cardiovascular Diseases, Cohort Studies, Denmark, Diabetes Mellitus, Type 2, Diabetes, Gestational, Female, Follow-Up Studies, Glycated Hemoglobin, Humans, Metabolic Diseases, Pregnancy, Prospective Studies, Risk Factors, Sweetening Agents, Women's Health, artificially sweetened beverages, nonnutritive sweeteners, soda, diet, obesity, diabetes, cardiometabolic health, gestational diabetes, Engineering, Medical and Health Sciences, Nutrition & Dietetics, Clinical sciences, Nutrition and dietetics
Abstract

BackgroundArtificially sweetened beverages (ASBs) are commonly consumed and recommended for individuals at high risk for cardiometabolic diseases; however, the health effects of ASBs remain contradictory. Given that cross-sectional analyses are subject to reverse causation, prospective studies with long-term follow-up are needed to evaluate associations between ASBs and cardiometabolic health, especially among high-risk individuals.ObjectiveThe aim of this study was to examine associations of ASB intake and cardiometabolic health among high-risk women with prior gestational diabetes mellitus (GDM).MethodsWe included 607 women with GDM from the Danish National Birth Cohort (DNBC; 1996-2002) who completed a clinical exam 9-16 y after the DNBC pregnancy for the Diabetes & Women's Health (DWH) Study (2012-2014). We assessed ASB intake using FFQs completed during the DNBC pregnancy and at the DWH Study clinical exam. We examined cardiometabolic outcomes at the DWH clinical exam. We estimated percentage differences in continuous cardiometabolic markers and RRs for clinical endpoints in association with ASB intake both during pregnancy and at follow-up adjusted for prepregnancy BMI, diet, and lifestyle factors. Sensitivity analyses to account for reverse causation were performed.ResultsIn pregnancy and at follow-up, 30.4% and 36.4% of women regularly (≥2 servings/wk) consumed ASB, respectively. Consumption of ASBs, both during pregnancy and at follow-up, was associated with higher glycated hemoglobin (HbA1c), insulin, HOMA-IR, triglycerides, liver fat, and adiposity and with lower HDL at follow-up. After adjustment for covariates, particularly prepregnancy BMI, the majority of associations between ASB intake in pregnancy and outcomes at follow-up became null with the exception of HbA1c. ASB intake at follow-up (≥1 serving/d compared with

Published
2019

19. Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group‐Tested Data.

Author

Yang, Jin, Zhang, Wei, Albert, Paul S., Liu, Aiyi, and Chen, Zhen

Abstract

We consider the problem of combining multiple biomarkers to improve the diagnostic accuracy of detecting a disease when only group‐tested data on the disease status are available. There are several challenges in addressing this problem, including unavailable individual disease statuses, differential misclassification depending on group size and number of diseased individuals in the group, and extensive computation due to a large number of possible combinations of multiple biomarkers. To tackle these issues, we propose a pairwise model fitting approach to estimating the distribution of the optimal linear combination of biomarkers and its diagnostic accuracy under the assumption of a multivariate normal distribution. The approach is evaluated in simulation studies and applied to data on chlamydia detection and COVID‐19 diagnosis. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Work and family conflicts, depressive symptoms and coparenting conflict behaviours: An interdependent approach.

Author

Ren, Yizhen, Liu, Aiyi, Zou, Shengqi, Ying, Jiefeng, Wang, Xinyi, and Wu, Xinchun

Subjects
*WORK, *PEARSON correlation (Statistics), *FAMILY conflict, *RESEARCH funding, *CRONBACH'S alpha, *WORK-life balance, *MOTHERS, *QUESTIONNAIRES, *PARENTING, *DESCRIPTIVE statistics, *FAMILY relations, *SURVEYS, *FATHERS, *CONCEPTUAL structures, *SELF-report inventories, *PSYCHOLOGY of parents, *FACTOR analysis, *CONFIDENCE intervals, *COVID-19 pandemic, *MENTAL depression, *WELL-being
Abstract

This study aims to investigate the relationships between work and family conflicts, specifically work‐to‐family and family‐to‐work conflicts, parental depressive symptoms and coparenting conflict behaviours during the COVID‐19 pandemic. This national survey study involved the participation of 830 families in mainland China, including fathers, mothers and adolescents. Fathers and mothers provided self‐reports on their experiences of work‐to‐family conflicts, family‐to‐work conflicts and depressive symptoms. Meanwhile, adolescents reported their perceptions of coparenting conflict behaviours exhibited by both fathers and mothers. The Actor‐Partner Interdependence Mediation Model was employed to analyse these relationships. In the Actor‐Partner Interdependence Mediation Model, paternal family‐to‐work conflicts were found to be positively associated with both paternal and maternal coparenting conflict behaviours, mediated through paternal depressive symptoms. Similarly, maternal family‐to‐work conflicts were positively linked to both paternal and maternal coparenting conflict behaviours, mediated through maternal depressive symptoms. Additionally, paternal family‐to‐work conflicts exhibited a direct positive relationship with both paternal and maternal coparenting conflict behaviours. However, paternal work‐to‐family conflicts demonstrated a direct negative association with paternal coparenting conflict behaviours. This study sheds light on the complex interconnectedness between work and family conflicts, parental depressive symptoms and coparenting conflict behaviours during the COVID‐19 pandemic. The findings highlight the importance of addressing work and family conflicts in understanding and managing coparenting dynamics, particularly during challenging times such as a pandemic. Such insights can inform interventions and support systems to promote healthier coparenting relationships and family well‐being. [ABSTRACT FROM AUTHOR]

Published
2024
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21. On the Optimal Combination of Elliptically Distributed Biomarkers to Improve Diagnostic Accuracy.

Author

Dong, Shiqi, Li, Zhaohai, Li, Yuanzhang, and Liu, Aiyi

Subjects
NEURAL tube defects, RECEIVER operating characteristic curves, AUTISM spectrum disorders, BIOMARKERS, MEDICAL research
Abstract

Diagnostic biomarkers play a critical role in biomedical research, particularly for the diagnosis and prediction of diseases, etc. To enhance diagnostic accuracy, extensive research about combining multiple biomarkers has been developed based on the multivariate normality, which is often not true in practice, as most biomarkers follow distributions that deviate from normality. While the likelihood ratio combination is recognized to be the optimal approach, it is complicated to calculate. To achieve a more accurate and effective combination of biomarkers, especially when these biomarkers deviate from normality, we propose using a receiver operating characteristic (ROC) curve methodology based on the optimal combination of elliptically distributed biomarkers. In this paper, we derive the ROC curve function for the elliptical likelihood ratio combination. Further, proceeding from the derived best combinations of biomarkers, we propose an efficient technique via nonparametric maximum likelihood estimate (NPMLE) to build empirical estimation. Simulation results show that the proposed elliptical combination method consistently provided better performance, demonstrating its robustness in handling various distribution types of biomarkers. We apply the proposed method to two real datasets: Autism/autism spectrum disorder (ASD) and neural tube defects (NTD). In both applications, the elliptical likelihood ratio combination improves the AUC value compared to the multivariate normal likelihood ratio combination and the best linear combination. [ABSTRACT FROM AUTHOR]

Published
2024
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22. An efficient and doubly robust empirical likelihood approach for estimating equations with missing data

Author

Liu, Tianqing, Yuan, Xiaohui, Li, Zhaohai, and Liu, Aiyi

Subjects
Statistics - Methodology
Abstract

This paper considers an empirical likelihood inference for parameters defined by general estimating equations, when data are missing at random. The efficiency of existing estimators depends critically on correctly specifying the conditional expectation of the estimating function given the observed components of the random observations. When the conditional expectation is not correctly specified, the efficiency of estimation can be severely compromised even if the propensity function (of missingness) is correctly specified. We propose an efficient estimator which enjoys the double-robustness property and can achieve the semiparametric efficiency bound within the class of the estimating functions that are generated by the estimating function of estimating equations, if both the propensity model and the regression model (of the conditional expectation) are specified correctly. Moreover, if the propensity model is specified correctly but the regression model is misspecified, the proposed estimator still achieves a semiparametric efficiency lower bound within a more general class of estimating functions. Simulation results suggest that the proposed estimators are robust against misspecification of the propensity model or regression model and outperform many existing competitors in the sense of having smaller mean-square errors. Moreover, using our approach for statistical inference requires neither resampling nor kernel smoothing. A real data example is used to illustrate the proposed approach., Comment: 31 pages,0 figures,7 tables

Published
2016

27. Nutrition during Pregnancy: Findings from the National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies–Singleton Cohort

Author

Hinkle, Stefanie N, Zhang, Cuilin, Grantz, Katherine L, Sciscione, Anthony, Wing, Deborah A, Grobman, William A, Newman, Roger B, D’Alton, Mary E, Skupski, Daniel, Nageotte, Michael P, Ranzini, Angela C, Owen, John, Chein, Edward K, Craigo, Sabrina, Yisahak, Samrawit F, Liu, Aiyi, Albert, Paul S, Louis, Germaine M Buck, and Grewal, Jagteshwar

Published
2021
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28. The use of covariates and random effects in evaluating predictive biomarkers under a potential outcome framework

Author

Zhang, Zhiwei, Nie, Lei, Soon, Guoxing, and Liu, Aiyi

Subjects
Statistics - Applications
Abstract

Predictive or treatment selection biomarkers are usually evaluated in a subgroup or regression analysis with focus on the treatment-by-marker interaction. Under a potential outcome framework (Huang, Gilbert and Janes [Biometrics 68 (2012) 687-696]), a predictive biomarker is considered a predictor for a desirable treatment benefit (defined by comparing potential outcomes for different treatments) and evaluated using familiar concepts in prediction and classification. However, the desired treatment benefit is unobservable because each patient can receive only one treatment in a typical study. Huang et al. overcome this problem by assuming monotonicity of potential outcomes, with one treatment dominating the other in all patients. Motivated by an HIV example that appears to violate the monotonicity assumption, we propose a different approach based on covariates and random effects for evaluating predictive biomarkers under the potential outcome framework. Under the proposed approach, the parameters of interest can be identified by assuming conditional independence of potential outcomes given observed covariates, and a sensitivity analysis can be performed by incorporating an unobserved random effect that accounts for any residual dependence. Application of this approach to the motivating example shows that baseline viral load and CD4 cell count are both useful as predictive biomarkers for choosing antiretroviral drugs for treatment-naive patients., Comment: Published in at http://dx.doi.org/10.1214/14-AOAS773 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2015
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33. Nonparametric ROC Summary Statistics for Correlated Diagnostic Marker Data

Author

Tang, Liansheng Larry, Liu, Aiyi, Chen, Zhen, Schisterman, Enrique F., Zhang, Bo, and Miao, Zhuang

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

We propose efficient nonparametric statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. The asymptotic results of the proposed methods are developed under a complex correlation structure. Our simulation studies show that the proposed statistics result in much better powers than existing statistics. We applied the proposed statistics to an endometriosis diagnosis study., Comment: 13 pages, 4 tables

Published
2012

34. Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.

Author

Du, Zhiyuan, Du, Pang, and Liu, Aiyi

Subjects
AUTISM in children, AUTISM spectrum disorders, RECEIVER operating characteristic curves, SPLINES, BIOMARKERS
Abstract

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non‐parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non‐diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real‐world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Rare copy number variants identified in prune belly syndrome

Author

Boghossian, Nansi S., Sicko, Robert J., Giannakou, Andreas, Dimopoulos, Aggeliki, Caggana, Michele, Tsai, Michael Y., Yeung, Edwina H., Pankratz, Nathan, Cole, Benjamin R., Romitti, Paul A., Browne, Marilyn L., Fan, Ruzong, Liu, Aiyi, Kay, Denise M., and Mills, James L.

Published
2018
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38. Depression and parenting in youth with type 1 diabetes: Are general and diabetes-specific parenting behaviors associated with depressive symptoms over a 2-year period?

Author

Dempster, Katherine W., Liu, Aiyi, and Nansel, Tonja R.

Subjects
Chronic diseases -- Care and treatment -- Complications and side effects, Type 1 diabetes -- Psychological aspects, Depression, Mental -- Risk factors, Parenting -- Psychological aspects, Adolescence -- Health aspects, Psychology and mental health
Abstract

To examine cross-sectional and longitudinal associations of general parenting style and diabetes-specific parenting behaviors with depression in youth with type 1 diabetes. Participants (n = 390) completed self-report measures of depression at baseline and 2-year follow-up, general parenting style at baseline, and diabetes-specific parenting (conflict, task involvement, and collaborative involvement) at baseline and every 6 months. Logistic regression examined associations of parenting with depression at baseline and 2-year follow-up. A less authoritative parenting style, lower parent collaborative involvement, and greater diabetes-related conflict were associated with baseline depression in the model simultaneously including all parenting variables and covariates. Lower parent collaborative involvement and higher diabetes-related conflict were associated with depression at 2-year follow-up, adjusting for baseline depression and covariates. Parent task involvement was not associated with depression at either time. Findings suggest a protective role of parenting in reducing the risk of depression in youth with type 1 diabetes., Author(s): Katherine W. Dempster [sup.1] , Aiyi Liu [sup.1] , Tonja R. Nansel [sup.1] Author Affiliations: (Aff1) 0000 0000 9635 8082, grid.420089.7, Division of Intramural Population Health Research, Social and [...]

Published
2019
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