Browsing by Author "Liu, Hai"
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Item Adiposity has unique influence on the renin-aldosterone axis and blood pressure in black children(Elsevier, 2013-11) Yu, Zhangsheng; Eckert, George; Liu, Hai; Pratt, J. Howard; Tu, Wanzhu; Medicine, School of MedicineOBJECTIVE: To comparatively examine the effects of adiposity on the levels of plasma renin activity (PRA), plasma aldosterone concentration (PAC), and aldosterone-renin ratio (ARR) in young black and white children. STUDY DESIGN: We prospectively assessed 248 black and 345 white children and adolescents. A novel analytical technique was used to assess the concurrent influences of age and body mass index (BMI) on PRA, PAC, and ARR. The estimated effects were depicted by colored contour plots. RESULTS: In contrast to whites, blacks had lower PRA (2.76 vs 3.36 ng/mL/h; P < .001) and lower PAC (9.01 vs 14.59 ng/dL; P < .001). In blacks, BMI was negatively associated with PRA (P = .001), consistent with an association with a more expanded plasma volume; there was no association with PAC. In whites, BMI was positively associated with PAC (P = .005); we did not detect a BMI-PRA association. The effects of BMI on ARR were directionally similar in the two race groups but more pronounced in blacks. Mean systolic blood pressure was greater in blacks with lower PRA (P < .01), higher PAC (P = .015), and higher ARR (P = .49). CONCLUSIONS: An increase in adiposity was associated with a suppressed PRA in blacks and an increase in PAC in whites. The unique relationship between adiposity and renin-aldosterone axis in blacks suggests the possible existence of a population-specific mechanism characterized by volume expansion, which could in turn enhance the influences of adiposity on blood pressure in black children and adolescents.Item Assessment of First and Second Degree Relatives of Individuals With Bipolar Disorder Shows Increased Genetic Risk Scores in Both Affected Relatives and Young At-Risk Individuals(Wiley, 2015-10) Fullerton, Janice M.; Koller, Daniel L.; Edenberg, Howard J.; Foroud, Tatiana; Liu, Hai; Glowinski, Anne L.; McInnis, Melvin G.; Wilcox, Holly C.; Frankland, Andrew; Roberts, Gloria; Schofield, Peter R.; Mitchell, Philip B.; Nurnberger, John I.; Department of Biochemistry and Molecular Biology, IU School of MedicineRecent studies have revealed the polygenic nature of bipolar disorder (BP), and identified common risk variants associated with illness. However, the role of common polygenic risk in multiplex families has not previously been examined. The present study examined 249 European-ancestry families from the NIMH Genetics Initiative sample, comparing subjects with narrowly defined BP (excluding bipolar II and recurrent unipolar depression; n = 601) and their adult relatives without BP (n = 695). Unrelated adult controls (n = 266) were from the NIMH TGEN control dataset. We also examined a prospective cohort of young (12–30 years) offspring and siblings of individuals with BPI and BPII disorder (at risk; n = 367) and psychiatrically screened controls (n = 229), ascertained from five sites in the US and Australia and assessed with standardized clinical protocols. Thirty-two disease-associated SNPs from the PGC-BP Working Group report (2011) were genotyped and additive polygenic risk scores (PRS) derived. We show increased PRS in adult cases compared to unrelated controls (P = 3.4 × 10−5, AUC = 0.60). In families with a high-polygenic load (PRS score ≥32 in two or more subjects), PRS distinguished cases with BPI/SAB from other relatives (P = 0.014, RR = 1.32). Secondly, a higher PRS was observed in at-risk youth, regardless of affected status, compared to unrelated controls (GEE-χ2 = 5.15, P = 0.012). This report is the first to explore common polygenic risk in multiplex families, albeit using only a small number of robustly associated risk variants. We show that individuals with BP have a higher load of common disease-associated variants than unrelated controls and first-degree relatives, and illustrate the potential utility of PRS assessment in a family context.Item Comprehensive evaluation of caregiver-reported antiretroviral therapy adherence for HIV-infected children(Springer-Verlag, 2015-04) Vreeman, Rachel C.; Nyandiko, Winstone M.; Liu, Hai; Tu, Wanzhu; Scanlon, Michael L.; Slaven, James E.; Ayaya, Samuel O.; Inui, Thomas S.; Department of Pediatrics, IU School of MedicineFor HIV-infected children, adherence to antiretroviral therapy (ART) is often assessed by caregiver report but there are few data on their validity. We conducted prospective evaluations with 191 children ages 0-14 years and their caregivers over 6 months in western Kenya to identify questionnaire items that best predicted adherence to ART. Medication Event Monitoring Systems(®) (MEMS, MWV/AARDEX Ltd., Switzerland) electronic dose monitors were used as external criterion for adherence. We employed a novel variable selection tool using the LASSO technique with logistic regression to identify items best correlated with dichotomized MEMS adherence (≥90 or <90 % doses taken). Nine of 48 adherence items were identified as the best predictors of adherence, including missed or late doses in the past 7 days, problems giving the child medicines, and caregiver-level factors like not being present at medication taking. These items could be included in adherence assessment tools for pediatric patients.Item Confirmatory test of two factors and four subtypes of bipolar disorder based on lifetime psychiatric comorbidity(Cambridge, 2015-07) Monahan, Patrick O.; Stump, Timothy; Coryell, William H.; Harezlak, Jaroslaw; Marcoulides, George A.; Liu, Hai; Steeger, Christine M.; Mitchell, Philip B.; Wilcox, Holly C.; Hulvershorn, Leslie A.; Glowinski, Anne L.; Iyer-Eimerbrink, Priya Anapurna; McInnis, Melvin; Nurnberger, John I. Jr.; Department of Biostatistics, IU School of MedicineBackground The first aim was to use confirmatory factor analysis (CFA) to test a hypothesis that two factors (internalizing and externalizing) account for lifetime co-morbid DSM-IV diagnoses among adults with bipolar I (BPI) disorder. The second aim was to use confirmatory latent class analysis (CLCA) to test the hypothesis that four clinical subtypes are detectible: pure BPI; BPI plus internalizing disorders only; BPI plus externalizing disorders only; and BPI plus internalizing and externalizing disorders. Method A cohort of 699 multiplex BPI families was studied, ascertained and assessed (1998–2003) by the National Institute of Mental Health Genetics Initiative Bipolar Consortium: 1156 with BPI disorder (504 adult probands; 594 first-degree relatives; and 58 more distant relatives) and 563 first-degree relatives without BPI. Best-estimate consensus DSM-IV diagnoses were based on structured interviews, family history and medical records. MPLUS software was used for CFA and CLCA. Results The two-factor CFA model fit the data very well, and could not be improved by adding or removing paths. The four-class CLCA model fit better than exploratory LCA models or post-hoc-modified CLCA models. The two factors and four classes were associated with distinctive clinical course and severity variables, adjusted for proband gender. Co-morbidity, especially more than one internalizing and/or externalizing disorder, was associated with a more severe and complicated course of illness. The four classes demonstrated significant familial aggregation, adjusted for gender and age of relatives. Conclusions The BPI two-factor and four-cluster hypotheses demonstrated substantial confirmatory support. These models may be useful for subtyping BPI disorders, predicting course of illness and refining the phenotype in genetic studies.Item Flexible models of time-varying exposures(2015-05) Wang, Chenkun; Gao, Sujuan; Liu, Hai; Yu, Zhangsheng; Callahan, Christopher M.With the availability of electronic medical records, medication dispensing data offers an unprecedented opportunity for researchers to explore complex relationships among longterm medication use, disease progression and potential side-effects in large patient populations. However, these data also pose challenges to existing statistical models because both medication exposure status and its intensity vary over time. This dissertation focused on flexible models to investigate the association between time-varying exposures and different types of outcomes. First, a penalized functional regression model was developed to estimate the effect of time-varying exposures on multivariate longitudinal outcomes. Second, for survival outcomes, a regression spline based model was proposed in the Cox proportional hazards (PH) framework to compare disease risk among different types of time-varying exposures. Finally, a penalized spline based Cox PH model with functional interaction terms was developed to estimate interaction effect between multiple medication classes. Data from a primary care patient cohort are used to illustrate the proposed approaches in determining the association between antidepressant use and various outcomes.Item A generalized semiparametric mixed model for analysis of multivariate health care utilization data(Sage, 2018-12) Li, Zhuokai; Liu, Hai; Tu, Wanzhu; Biostatistics, School of Public HealthHealth care utilization is an outcome of interest in health services research. Two frequently studied forms of utilization are counts of emergency department (ED) visits and hospital admissions. These counts collectively convey a sense of disease exacerbation and cost escalation. Different types of event counts from the same patient form a vector of correlated outcomes. Traditional analysis typically model such outcomes one at a time, ignoring the natural correlations between different events, and thus failing to provide a full picture of patient care utilization. In this research, we propose a multivariate semiparametric modeling framework for the analysis of multiple health care events following the exponential family of distributions in a longitudinal setting. Bivariate nonparametric functions are incorporated to assess the concurrent nonlinear influences of independent variables as well as their interaction effects on the outcomes. The smooth functions are estimated using the thin plate regression splines. A maximum penalized likelihood method is used for parameter estimation. The performance of the proposed method was evaluated through simulation studies. To illustrate the method, we analyzed data from a clinical trial in which ED visits and hospital admissions were considered as bivariate outcomes.Item A high-risk study of bipolar disorder. Childhood clinical phenotypes as precursors of major mood disorders(AMA, 2011-10) Nurnberger, John I. Jr.; McInnis, Melvin; Reich, Wendy; Kastelic, Elizabeth; Wilcox, Holly C.; Glowinski, Glowinski; Mitchell, Philip; Fisher, Carrie; Erpe, Mariano; Gershon, Elliot S.; Berrettini, Wade; Laite, Gina; Schweitzer, Robert; Rhoadarmer, Kelly; Coleman, Vegas V.; Cai, Xueya; Azzouz, Faouzi; Liu, Hai; Kamali, Masoud; Brucksch, Christine; Monahan, Patrick O.; Department of Medicine, IU School of MedicineCONTEXT: The childhood precursors of adult bipolar disorder (BP) are still a matter of controversy. OBJECTIVE: To report the lifetime prevalence and early clinical predictors of psychiatric disorders in offspring from families of probands with DSM-IV BP compared with offspring of control subjects. DESIGN: A longitudinal, prospective study of individuals at risk for BP and related disorders. We report initial (cross-sectional and retrospective) diagnostic and clinical characteristics following best-estimate procedures. SETTING: Assessment was performed at 4 university medical centers in the United States between June 1, 2006, and September 30, 2009. PARTICIPANTS: Offspring aged 12 to 21 years in families with a proband with BP (n = 141, designated as cases) and similarly aged offspring of control parents (n = 91). MAIN OUTCOME MEASURE: Lifetime DSM-IV diagnosis of a major affective disorder (BP type I; schizoaffective disorder, bipolar type; BP type II; or major depression). RESULTS: At a mean age of 17 years, cases showed a 23.4% lifetime prevalence of major affective disorders compared with 4.4% in controls (P = .002, adjusting for age, sex, ethnicity, and correlation between siblings). The prevalence of BP in cases was 8.5% vs 0% in controls (adjusted P = .007). No significant difference was seen in the prevalence of other affective, anxiety, disruptive behavior, or substance use disorders. Among case subjects manifesting major affective disorders (n = 33), there was an increased risk of anxiety and externalizing disorders compared with cases without mood disorder. In cases but not controls, a childhood diagnosis of an anxiety disorder (relative risk = 2.6; 95% CI, 1.1-6.3; P = .04) or an externalizing disorder (3.6; 1.4-9.0; P = .007) was predictive of later onset of major affective disorders. CONCLUSIONS: Childhood anxiety and externalizing diagnoses predict major affective illness in adolescent offspring in families with probands with BP.Item Introducing COZIGAM: An R Package for Unconstrained and Constrained Zero-Inflated Generalized Additive Model Analysis(Foundation for Open Access Statistics, 2010-07-26) Liu, Hai; Chan, Kung-Sik; Medicine, School of MedicineZero-inflation problem is very common in ecological studies as well as other areas. Nonparametric regression with zero-inflated data may be studied via the zero-inflated generalized additive model (ZIGAM), which assumes that the zero-inflated responses come from a probabilistic mixture of zero and a regular component whose distribution belongs to the 1-parameter exponential family. With the further assumption that the probability of non-zero-inflation is some monotonic function of the mean of the regular component, we propose the constrained zero-inflated generalized additive model (COZIGAM) for analyzingzero-inflated data. When the hypothesized constraint obtains, the new approach provides a unified framework for modeling zero-inflated data, which is more parsimonious and efficient than the unconstrained ZIGAM. We have developed an R package COZIGAM which contains functions that implement an iterative algorithm for fitting ZIGAMs and COZIGAMs to zero-inflated data basedon the penalized likelihood approach. Other functions included in the packageare useful for model prediction and model selection. We demonstrate the use ofthe COZIGAM package via some simulation studies and a real application.Item Measuring adherence to antiretroviral therapy in children and adolescents in western Kenya(2014-11) Vreeman, Rachel C.; Nyandiko, Winstone M.; Liu, Hai; Tu, Wanzhu; Scanlon, Michael L.; Slaven, James E.; Ayaya, Samuel O.; Inui, Thomas S.; Department of Pediatrics, Indiana University School of MedicineIntroduction: High levels of adherence to antiretroviral therapy (ART) are central to HIV management. The objective of this study was to compare multiple measures of adherence and investigate factors associated with adherence among HIV-infected children in western Kenya. Methods: We evaluated ART adherence prospectively for six months among HIV-infected children aged ≤14 years attending a large outpatient HIV clinic in Kenya. Adherence was reported using caregiver report, plasma drug concentrations and Medication Event Monitoring Systems (MEMS®). Kappa statistics were used to compare adherence estimates with MEMS®. Logistic regression analyses were performed to assess the association between child, caregiver and household characteristics with dichotomized adherence (MEMS® adherence ≥90% vs. <90%) and MEMS® treatment interruptions of ≥48 hours. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated. Results: Among 191 children, mean age at baseline was 8.2 years and 55% were female. Median adherence by MEMS® was 96.3% and improved over the course of follow-up (p<0.01), although 49.5% of children had at least one MEMS® treatment interruption of ≥48 hours. Adherence estimates were highest by caregiver report, and there was poor agreement between MEMS® and other adherence measures (Kappa statistics 0.04–0.37). In multivariable logistic regression, only caregiver-reported missed doses in the past 30 days (OR 1.25, 95% CI 1.14–1.39), late doses in the past seven days (OR 1.14, 95% CI 1.05–1.22) and caregiver-reported problems with getting the child to take ART (OR 1.10, 95% CI 1.01–1.20) were significantly associated with dichotomized MEMS® adherence. The caregivers reporting that ART made the child sick (OR 1.12, 95% CI 1.01–1.25) and reporting difficulties in the community that made giving ART more difficult (e.g. stigma) (OR 1.14, 95% CI 1.02–1.27) were significantly associated with MEMS® treatment interruptions in multivariable logistic regression. Conclusions: Non-adherence in the form of missed and late doses, treatment interruptions of more than 48 hours and sub-therapeutic drug levels were common in this cohort. Adherence varied significantly by adherence measure, suggesting that additional validation of adherence measures is needed. Few factors were consistently associated with non-adherence or treatment interruptions.Item Multivariate semiparametric regression models for longitudinal data(2014) Li, Zhuokai; Tu, Wanzhu; Liu, Hai; Katz, Barry P.; Fortenberry, J. DennisMultiple-outcome longitudinal data are abundant in clinical investigations. For example, infections with different pathogenic organisms are often tested concurrently, and assessments are usually taken repeatedly over time. It is therefore natural to consider a multivariate modeling approach to accommodate the underlying interrelationship among the multiple longitudinally measured outcomes. This dissertation proposes a multivariate semiparametric modeling framework for such data. Relevant estimation and inference procedures as well as model selection tools are discussed within this modeling framework. The first part of this research focuses on the analytical issues concerning binary data. The second part extends the binary model to a more general situation for data from the exponential family of distributions. The proposed model accounts for the correlations across the outcomes as well as the temporal dependency among the repeated measures of each outcome within an individual. An important feature of the proposed model is the addition of a bivariate smooth function for the depiction of concurrent nonlinear and possibly interacting influences of two independent variables on each outcome. For model implementation, a general approach for parameter estimation is developed by using the maximum penalized likelihood method. For statistical inference, a likelihood-based resampling procedure is proposed to compare the bivariate nonlinear effect surfaces across the outcomes. The final part of the dissertation presents a variable selection tool to facilitate model development in practical data analysis. Using the adaptive least absolute shrinkage and selection operator (LASSO) penalty, the variable selection tool simultaneously identifies important fixed effects and random effects, determines the correlation structure of the outcomes, and selects the interaction effects in the bivariate smooth functions. Model selection and estimation are performed through a two-stage procedure based on an expectation-maximization (EM) algorithm. Simulation studies are conducted to evaluate the performance of the proposed methods. The utility of the methods is demonstrated through several clinical applications.