Dynamic hierarchical state space forecasting.

In this paper, we aim to both borrow information from existing units and incorporate the target unit's history data in time series forecasting. We consider a situation when we have time series data from multiple units that share similar patterns when aligned in terms of an internal time. The internal time is defined as an index according to evolving features of interest. When mapped back to the calendar time, these time series can span different time intervals that can include the future calendar time of the targeted unit, over which we can borrow the information from other units in forecasting the targeted unit. We first build a hierarchical state space model for the multiple time series data in terms of the internal time, where the shared components capture the similarities among different units while allowing for unit-specific deviations. A conditional state space model is then constructed to incorporate the information of existing units as the prior information in forecasting the targeted unit. By running the Kalman filtering based on the conditional state space model on the targeted unit, we incorporate both the information from the other units and the history of the targeted unit. The forecasts are then transformed from internal time back into calendar time for ease of interpretation. A simulation study is conducted to evaluate the finite sample performance. Forecasting state-level new COVID-19 cases in United States is used for illustration.

Selection of number of clusters and warping penalty in clustering functional electrocardiogram.

Clustering functional data aims to identify unique functional patterns in the entire domain, but this can be challenging due to phase variability that distorts the observed patterns. Curve registration can be used to remove this variability, but determining the appropriate level of warping flexibility can be complicated. Curve registration also requires a target to which a functional object is aligned, typically the cross-sectional mean of functional objects within the same cluster. However, this mean is unknown prior to clustering. Furthermore, there is a trade-off between flexible warping and the number of resulting clusters. Removing more phase variability through curve registration can lead to fewer remaining variations in the functional data, resulting in a smaller number of clusters. Thus, the optimal number of clusters and warping flexibility cannot be uniquely identified. We propose to use external information to solve the identification issue. We define a cross validated Kullback-Leibler information criterion to select the number of clusters and the warping penalty. The criterion is derived from the predictive classification likelihood considering the joint distribution of both the functional data and external variable and penalizes the uncertainty in the cluster membership. We evaluate our method through simulation and apply it to electrocardiographic data collected in the Chronic Renal Insufficiency Cohort study. We identify two distinct clusters of electrocardiogram (ECG) profiles, with the second cluster exhibiting ST segment depression, an indication of cardiac ischemia, compared to the normal ECG profiles in the first cluster.

Dynamic logistic state space prediction model for clinical decision making.

Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time-varying and time-invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying binomial density function. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply the method to predict 1 year survival after lung transplantation using the United Network for Organ Sharing data.

Predicting age at Alzheimer's dementia onset with the cognitive clock.

INTRODUCTION: Intervention of Alzheimer's dementia hinges on early diagnosis and advanced planning. This work utilizes the cognitive clock, a novel indicator of brain health, to develop a dementia prediction model that can be easily applied in broad settings. METHODS: Data came from over 3000 community-dwelling older adults. Cognitive age was estimated by aligning Mini-Mental State Examination (MMSE) scores to a clock that represents the typical cognitive aging profile. We identified a mean cognitive age at Alzheimer's dementia onset and predicted the corresponding chronological age at person-specific level. RESULTS: The mean chronological age at baseline was 78 years. A total of 881 (28%) participants developed Alzheimer's dementia. The mean cognitive age at onset was 91 years. The predicted chronological age at onset had a mean (standard deviation) of 87.6 (6.7) years. The model's prediction accuracy was supported by multiple testing statistics. DISCUSSION: Our model offers an easy-to-use tool for predicting person-specific age at Alzheimer's dementia onset.

Acute e-cig inhalation impacts vascular health: a study in smoking naïve subjects.

This study was designed to investigate the acute effects of nonnicotinized e-cigarette (e-cig) aerosol inhalation in nonsmokers both in terms of blood-based markers of inflammation and oxidative stress and evaluate their association with hemodynamic-metabolic MRI parameters quantifying peripheral vascular reactivity, cerebrovascular reactivity, and aortic stiffness. Thirty-one healthy nonsmokers were subjected to two blood draws and two identical MRI protocols, each one before and after a standardized e-cig vaping session. After vaping, the serum levels of C-reactive protein, soluble intercellular adhesion molecule, and the danger signal machinery high-mobility group box 1 (HMGB1) and its downstream effector and the NLR family pyrin domain containing 3 (NLRP3) inflammasome (as monitored by its adaptor protein ASC) increased significantly relative to the respective baseline (prevaping) values. Moreover, nitric oxide metabolites and reactive oxygen species production decreased and increased, respectively. These observations were paralleled by impaired peripheral vascular reactivity (with reduced flow-mediated dilation and attenuated hyperemic response after a cuff-occlusion test) and metabolic alterations expressed by decreased venous oxygen saturation, postvaping. The current results suggest propagation of inflammation signaling via activation of the danger signaling axis (HMGB1-NLRP3). The findings indicate that a single episode of vaping has adverse impacts on vascular inflammation and function.NEW & NOTWORTHY Endothelial cell signaling and blood biomarkers were found to correlate with functional vascular changes in a single episode e-cigarettes inhalation in healthy adults. This is indicative of the potential of e-cigarettes (even when inhaled acutely) to lead of vascular dysfunction.

Acute exposure to e-cigarettes causes inflammation and pulmonary endothelial oxidative stress in nonsmoking, healthy young subjects.

The effects of e-cigarette (e-cig) aerosol inhalation by nonsmokers have not been examined to date. The present study was designed to evaluate the acute response to aerosol inhalation of non-nicotinized e-cigarettes in terms of oxidative stress and indices of endothelial activation in human pulmonary microvascular endothelial cells (HPMVEC). Ten smoking-naïve healthy subjects (mean age ± SD = 28.7 ± 5.5 yr) were subjected to an e-cig challenge, following which their serum was monitored for markers of inflammation [C-reactive protein (CRP) and soluble intercellular adhesion molecule (sICAM)] and nitric oxide metabolites (NOx). The oxidative stress and inflammation burden of the circulating serum on the vascular network was also assessed by measuring reactive oxygen species (ROS) production and induction of ICAM-1 expression on HPMVEC. Our results show that serum indices of oxidative stress and inflammation increased significantly (P < 0.05 as compared with baseline), reaching a peak at approximately 1-2 h post-e-cig aerosol inhalation and returning to baseline levels at 6 h. The circulatory burden of the serum (ICAM-1 and ROS) increased significantly at 2 h and returned to baseline values 6 h post-e-cig challenge. ROS production by HPMVEC was found to occur via activation of the NADPH oxidase 2 (NOX2) pathways. These findings suggest that even in the absence of nicotine, acute e-cig aerosol inhalation leads to a transient increase in oxidative stress and inflammation. This can adversely affect the vascular endothelial network by promoting oxidative stress and immune cell adhesion. Thus e-cig inhalation has the potential to drive the onset of vascular pathologies.

Acute Effects of Electronic Cigarette Aerosol Inhalation on Vascular Function Detected at Quantitative MRI.

Background Previous studies showed that nicotinized electronic cigarettes (hereafter, e-cigarettes) elicit systemic oxidative stress and inflammation. However, the effect of the aerosol alone on endothelial function is not fully understood. Purpose To quantify surrogate markers of endothelial function in nonsmokers after inhalation of aerosol from nicotine-free e-cigarettes. Materials and Methods In this prospective study (from May to September 2018), nonsmokers underwent 3.0-T MRI before and after inhaling nicotine-free e-cigarette aerosol. Peripheral vascular reactivity to cuff-induced ischemia was quantified by temporally resolving blood flow velocity and oxygenation (SvO2) in superficial femoral artery and vein, respectively, along with artery luminal flow-mediated dilation. Precuff occlusion, resistivity index, baseline blood flow velocity, and SvO2 were evaluated. During reactive hyperemia, blood flow velocity yielded peak velocity, time to peak, and acceleration rate (hyperemic index); SvO2 yielded washout time of oxygen-depleted blood, rate of resaturation, and maximum SvO2 increase (overshoot). Cerebrovascular reactivity was assessed in the superior sagittal sinus, evaluating the breath-hold index. Central arterial stiffness was measured via aortic pulse wave velocity. Differences before versus after e-cigarette vaping were tested with Hotelling T2 test. Results Thirty-one healthy never-smokers (mean age, 24.3 years ± 4.3; 14 women) were evaluated. After e-cigarette vaping, resistivity index was higher (0.03 of 1.30 [2.3%]; P < .05), luminal flow-mediated dilation severely blunted (-3.2% of 9.4% [-34%]; P < .001), along with reduced peak velocity (-9.9 of 56.6 cm/sec [-17.5%]; P < .001), hyperemic index (-3.9 of 15.1 cm/sec2 [-25.8%]; P < .001), and delayed time to peak (2.1 of 7.1 sec [29.6%]; P = .005); baseline SvO2 was lower (-13 of 65 %HbO2 [-20%]; P < .001) and overshoot higher (10 of 19 %HbO2 [52.6%]; P < .001); and aortic pulse wave velocity marginally increased (0.19 of 6.05 m/sec [3%]; P = .05). Remaining parameters did not change after aerosol inhalation. Conclusion Inhaling nicotine-free electronic cigarette aerosol transiently impacted endothelial function in healthy nonsmokers. Further studies are needed to address the potentially adverse long-term effects on vascular health. © RSNA, 2019 Online supplemental material is available for this article.

Detection of Brain Hypoxia Based on Noninvasive Optical Monitoring of Cerebral Blood Flow with Diffuse Correlation Spectroscopy.

BACKGROUND: Diffuse correlation spectroscopy (DCS) noninvasively permits continuous, quantitative, bedside measurements of cerebral blood flow (CBF). To test whether optical monitoring (OM) can detect decrements in CBF producing cerebral hypoxia, we applied the OM technique continuously to probe brain-injured patients who also had invasive brain tissue oxygen (PbO2) monitors. METHODS: Comatose patients with a Glasgow Coma Score (GCS) < 8) were enrolled in an IRB-approved protocol after obtaining informed consent from the legally authorized representative. Patients underwent 6-8 h of daily monitoring. Brain PbO2 was measured with a Clark electrode. Absolute CBF was monitored with DCS, calibrated by perfusion measurements based on intravenous indocyanine green bolus administration. Variation of optical CBF and mean arterial pressure (MAP) from baseline was measured during periods of brain hypoxia (defined as a drop in PbO2 below 19 mmHg for more than 6 min from baseline (PbO2 > 21 mmHg). In a secondary analysis, we compared optical CBF and MAP during randomly selected 12-min periods of "normal" (> 21 mmHg) and "low" (< 19 mmHg) PbO2. Receiver operator characteristic (ROC) and logistic regression analysis were employed to assess the utility of optical CBF, MAP, and the two-variable combination, for discrimination of brain hypoxia from normal brain oxygen tension. RESULTS: Seven patients were enrolled and monitored for a total of 17 days. Baseline-normalized MAP and CBF significantly decreased during brain hypoxia events (p < 0.05). Through use of randomly selected, temporally sparse windows of low and high PbO2, we observed that both MAP and optical CBF discriminated between periods of brain hypoxia and normal brain oxygen tension (ROC AUC 0.761, 0.762, respectively). Further, combining these variables using logistic regression analysis markedly improved the ability to distinguish low- and high-PbO2 epochs (AUC 0.876). CONCLUSIONS: The data suggest optical techniques may be able to provide continuous individualized CBF measurement to indicate occurrence of brain hypoxia and guide brain-directed therapy.

Cerebral Blood Flow Response During Bolus Normal Saline Infusion After Ischemic Stroke.

GOALS: We quantified cerebral blood flow response to a 500 cc bolus of 0.9%% normal saline (NS) within 96 hours of acute ischemic stroke (AIS) using diffuse correlation spectroscopy (DCS). MATERIALS AND METHODS: Subjects with AIS in the anterior, middle, or posterior cerebral artery territory were enrolled within 96 hours of symptom onset. DCS measured relative cerebral blood flow (rCBF) in the bilateral frontal lobes for 15 minutes at rest (baseline), during a 30-minute infusion of 500 cc NS (bolus), and for 15 minutes after completion (post-bolus). Mean rCBF for each time period was calculated for individual subjects and median rCBF for the population was compared between time periods. Linear regression was used to evaluate for associations between rCBF and clinical features. RESULTS: Among 57 subjects, median rCBF (IQR) increased relative to baseline in the ipsilesional hemisphere by 17% (-2.0%, 43.1%), P< 0.001, and in the contralesional hemisphere by 13.3% (-4.3%, 36.0%), P < .004. No significant associations were found between ipsilesional changes in rCBF and age, race, infarct size, infarct location, presence of large vessel stenosis, NIH stroke scale, or symptom duration. CONCLUSION: A 500 cc bolus of .9% NS produced a measurable increase in rCBF in both the affected and nonaffected hemispheres. Clinical features did not predict rCBF response.

Adult Connection in Assault Injury Prevention among Male Youth in Low-Resource Urban Environments.

Strengths-based strategies to reduce youth violence in low-resource urban communities are urgently needed. Supportive adolescent-adult relationships may confer protection, but studies have been limited by self-reported composite outcomes. We conducted a population-based case-control study among 10- to 24-year-old males in low-resource neighborhoods to examine associations between supportive adult connection and severe assault injury. Cases were victims of gunshot assault injury (n = 143) and non-gun assault injury (n = 206) from two level I trauma centers. Age- and race-matched controls (n = 283) were recruited using random digit dial from the same catchment. Adolescent-adult connections were defined by: (1) brief survey questions and (2) detailed family genograms. Analysis used conditional logistic regression. There were no significant associations between positive adult connection, as defined by brief survey questions, and either gunshot or non-gun assault injury among adolescents with high prior violence involvement (GSW OR = 2.46, 95% CI 0.81-7.49; non-gun OR = 1.59, 95% CI 0.54-4.67) or low prior violence involvement (GSW OR = 0.92, 95% CI 0.34-2.44; non-gun OR = 1.96, 95% CI 0.73-5.28). In contrast, among adolescents with high levels of prior violence involvement, reporting at least one supportive adult family member in the family genogram was associated with higher odds of gunshot assault injury (OR = 4.01, 95% CI 1.36-11.80) and non-gun assault injury (OR = 4.22, 95% CI 1.48-12.04). We were thus unable to demonstrate that positive adult connections protected adolescent males from severe assault injury in this highly under-resourced environment. However, at the time of injury, assault-injured adolescents, particularly those with high prior violence involvement, reported high levels of family support. The post-injury period may provide opportunities to intervene to enhance and leverage family connections to explore how to better safeguard adolescents.

Clinical and Psychosocial Predictors of Urological Chronic Pelvic Pain Symptom Change in 1 Year: A Prospective Study from the MAPP Research Network.

PURPOSE: We examined baseline clinical and psychosocial characteristics that predict 12-month symptom change in men and women with urological chronic pelvic pain syndromes. MATERIALS AND METHODS: A total of 221 female and 176 male patients with urological chronic pelvic pain syndromes were recruited from 6 academic medical centers in the United States and evaluated at baseline with a comprehensive battery of symptom, psychosocial and illness-impact measures. Based on biweekly symptom reports, a functional clustering procedure classified participant outcome as worse, stable or improved on pain and urinary symptom severity. Cumulative logistic modeling was used to examine individual predictors associated with symptom change as well as multiple predictor combinations and interactions. RESULTS: About 60% of participants had stable symptoms with smaller numbers (13% to 22%) showing clear symptom worsening or improvement. For pain and urinary outcomes the extent of widespread pain, amount of nonurological symptoms and poorer overall health were predictive of worsening outcomes. Anxiety, depression and general mental health were not significant predictors of outcomes but pain catastrophizing and self-reported stress were associated with pain outcome. Prediction models did not differ between men and women and for the most part they were independent of symptom duration and age. CONCLUSIONS: These results demonstrate for the first time in a large multisite prospective study that presence of widespread pain, nonurological symptoms and poorer general health are risk factors for poorer pain and urinary outcomes in men and women. The results point to the importance of broad based assessment for urological chronic pelvic pain syndromes and future studies of the mechanisms that underlie these findings.

Mapping Activity Patterns to Quantify Risk of Violent Assault in Urban Environments.

BACKGROUND: We collected detailed activity paths of urban youth to investigate the dynamic interplay between their lived experiences, time spent in different environments, and risk of violent assault. METHODS: We mapped activity paths of 10- to 24-year-olds, including 143 assault patients shot with a firearm, 206 assault patients injured with other types of weapons, and 283 community controls, creating a step-by-step mapped record of how, when, where, and with whom they spent time over a full day from waking up until going to bed or being assaulted. Case-control analyses compared cases with time-matched controls to identify risk factors for assault. Case-crossover analyses compared cases at the time of assault with themselves earlier in the day to investigate whether exposure increases acted to the trigger assault. RESULTS: Gunshot assault risks included being alone (odds ratio [OR] = 1.6, 95% confidence interval [CI] = 1.3, 1.9) and were lower in areas with high neighbor connectedness (OR = 0.7, 95% CI = 0.6, 0.8). Acquiring a gun (OR = 1.4, 95% CI = 1.1, 1.6) and entering areas with more vacancy, violence, and vandalism (OR = 1.7, 95% CI = 1.1, 2.7) appeared to trigger the risk of getting shot shortly thereafter. Nongunshot assault risks included being in areas with recreation centers (OR = 1.2, 95% CI = 1.1, 1.4). Entering an area with higher truancy (OR = 1.6, 95% CI = 1.1, 2.5) and more vacancy, violence, and vandalism appeared to trigger the risk of nongunshot assault. Risks varied by age group. CONCLUSIONS: We achieved a large-scale study of the activities of many boys, adolescents, and young men that systematically documented their experiences and empirically quantified risks for violence. Working at a temporal and spatial scale that is relevant to the dynamics of this phenomenon gave novel insights into triggers for violent assault.

Modeling diurnal hormone profiles by hierarchical state space models.

Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing (1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls and (2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls.

A random pattern mixture model for ordinal outcomes with informative dropouts.

We extend a random pattern mixture joint model for longitudinal ordinal outcomes and informative dropouts. The patients are generalized to 'pattern' groups based on known covariates that are potentially surrogated for the severity of the underlying condition. The random pattern effects are defined as the latent effects linking the dropout process and the ordinal longitudinal outcome. Conditional on the random pattern effects, the longitudinal outcome and the dropout times are assumed independent. Estimates are obtained via the Expectation-maximization algorithm. We applied the model to the end-stage renal disease data. Anemia was found to be significantly affected by the baseline iron treatment when the dropout information was adjusted via the study model; as opposed to an independent or shared parameter model. Simulations were performed to evaluate the performance of the random pattern mixture model under various assumptions.

Effects of age and smoking on endothelial function assessed by quantitative cardiovascular magnetic resonance in the peripheral and central vasculature.

BACKGROUND: Both age and smoking promote endothelial dysfunction and impair vascular reactivity. Here, we tested this hypothesis by quantifying new cardiovascular magnetic resonance (CMR)-based biomarkers in smokers and nonsmokers. METHODS: Study population: young non-smokers (YNS: N = 45, mean age = 30.2 ± 0.7 years), young smokers (YS: N = 39 mean age 32.1 ± 0.7 years), older non-smokers (ONS: N = 45, mean age = 57.8 ± 0.6 years), and older smokers (OS: N = 40, mean age = 56.3 ± 0.6 years), all without overt cardiovascular disease. Vascular reactivity was evaluated following cuff-induced hyperemia via time-resolved blood flow velocity and oxygenation (SvO2) in the femoral artery and vein, respectively. SvO2 dynamics yielded washout time (time to minimum SvO2), resaturation rate (upslope) and maximum change from baseline (overshoot). Arterial parameters included pulse ratio (PR), hyperemic index (HI) and duration of hyperemia (TFF). Pulse-wave velocity (PWV) was assessed in aortic arch, thoracoabdominal aorta and iliofemoral arteries. Ultrasound-based carotid intimal-medial thickness (IMT) and brachial flow-mediated dilation were measured for comparison. RESULTS: Age and smoking status were independent for all parameters. Smokers had reduced upslope (-28.4%, P < 0.001), increased washout time (+15.3%, P < 0.01), and reduced HI (-19.5%, P < 0.01). Among non-smokers, older subjects had lower upslope (-22.7%, P < 0.01) and overshoot (-29.4%, P < 0.01), elevated baseline pulse ratio (+14.9%, P < 0.01), central and peripheral PWV (all P < 0.05). Relative to YNS, YS had lower upslope (-23.6%, P < 0.01) and longer washout time (13.5%, P < 0.05). Relative to ONS, OS had lower upslope (-33.0%, P < 0.01). IMT was greater in ONS than in YNS (+45.6%, P < 0.001), and also in YS compared to YNS (+14.7%, P < 0.05). CONCLUSIONS: Results suggest CMR biomarkers of endothelial function to be sensitive to age and smoking independent of each other.

The estimation of branching curves in the presence of subject-specific random effects.

Branching curves are a technique for modeling curves that change trajectory at a change (branching) point. Currently, the estimation framework is limited to independent data, and smoothing splines are used for estimation. This article aims to extend the branching curve framework to the longitudinal data setting where the branching point varies by subject. If the branching point is modeled as a random effect, then the longitudinal branching curve framework is a semiparametric nonlinear mixed effects model. Given existing issues with using random effects within a smoothing spline, we express the model as a B-spline based semiparametric nonlinear mixed effects model. Simple, clever smoothness constraints are enforced on the B-splines at the change point. The method is applied to Women's Health data where we model the shape of the labor curve (cervical dilation measured longitudinally) before and after treatment with oxytocin (a labor stimulant).

The effect of postinjury depression on quality of life following minor injury.

PURPOSE: To describe quality of life (QoL) in the year following minor injury and to test the hypothesis that individuals with depression in the postinjury year experience lower QoL than do individuals with no depression. DESIGN: Prospective, longitudinal, cohort design. A total of 275 adults were randomly selected from injured patients presenting to an urban emergency department. METHODS: All participants underwent structured psychiatric diagnostic interviews immediately after injury and at 3, 6, and 12 months. The primary outcome, QoL, was measured using the Quality of Life Index. Covariates included demographics, injury status, preinjury functional status, preinjury social support, and anticipation of problems postdischarge. The General Estimating Equation was used to compare changes in QoL between participants with and without depression over 3, 6, and 12 months, adjusting for covariates. RESULTS: An 18.1% proportion (95% confidence interval [CI] 13.3, 22.9%) of the sample met criteria for a mood disorder in the postinjury year. The depressed group reported a QoL that was 4.2 points (95% CI 2.8-5.6) lower in the year postinjury compared with that of the nondepressed group. CONCLUSIONS: Depression after minor injury negatively affects QoL even a full year postinjury. CLINICAL RELEVANCE: The findings of this study show that patients who have injuries that are treated and discharged from an emergency department can have significantly lower QoL in the year after that injury that is attributed, in part, to postinjury depression. Nurses should provide anticipatory guidance to patients that they may experience feelings of sadness or being "blue," and that if they do, they should seek care.

[Treatment of combined hyperlipidemia patients by jiangzhi tongluo soft capsule combined atorvastatin calcium tablet: a clinical study].

OBJECTIVE: To evaluate the efficacy and safety of using Jiangzhi Tongluo Soft Capsule (JTSC) combined with Atorvastatin Calcium Tablet (ACT) or ACT alone in treatment of combined hyperlipidemia. METHODS: A randomized, double blinded, parallel control, and multi-center clinical research design was adopted. Totally 138 combined hyperlipidemia patients were randomly assigned to the combined treatment group (A) and the atorvastatin treatment group (B) by random digit table, 69 in each group. All patients took ACT 20 mg per day. Patients in the A group took JTSC 100 mg each time, 3 times per day. Those in the B group took JTSC simulated agent, 100 mg each time, 3 times per day. The treatment period for all was 8 weeks. Serum levels of triglyceride (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C) were observed before treatment, at week 4 and 8 after treatment; and safety was assessed as well. RESULTS: At week 4 and 8 after treatment serum TG decreased by 26.69% and 33.29% respectively in the A group (both P < 0.01), while it was decreased by 25.7% and 22.98% respectively in the B group (both P < 0.01). At week 8 decreased serum TG was obviously higher in the A group than in the B group (P < 0.05). Compared with before treatment, serum levels of LDL-C and TC levels decreased significantly in the two groups (all P < 0.01). There was no statistical difference in the drop-out value and the drop-out rate of serum LDL-C and TC levels (P > 0.05). At week 8 the serum HDL-C level showed an increasing tendency in the two groups. No obvious increase in peptase or creatase occurred in the two groups after treatment. CONCLUSION: JTSC combined with ACT could lower the serum TG level of combined hyperlipidemia patients with safety.

SEMIPARAMETRIC BIVARIATE HIERARCHICAL STATE SPACE MODEL WITH APPLICATION TO HORMONE CIRCADIAN RELATIONSHIP.

The adrenocorticotropic hormone and cortisol play critical roles in stress regulation and the sleep-wake cycle. Most research has been focused on how the two hormones regulate each other in terms of short-term pulses. Few studies have been conducted on the circadian relationship between the two hormones and how it differs between normal and abnormal groups. The circadian patterns are difficult to model as parametric functions. Directly extending univariate functional mixed effects models would result in a large dimensional problem and a challenging nonparametric inference. In this article, we propose a semi-parametric bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with nonparametric population-average and subject-specific components. The bivariate relationship is constructed by concatenating two latent independent subject-specific random functions specified by a design matrix, leading to a parametric inference on the correlation. We propose a computationally efficient state-space EM algorithm for estimation and inference. We apply the proposed method to a study of chronic fatigue syndrome and fibromyalgia and discover an erratic regulation pattern in the patient group in contrast to a circadian regulation pattern conforming to the day-night cycle in the control group.

A NONPARAMETRIC MIXED-EFFECTS MIXTURE MODEL FOR PATTERNS OF CLINICAL MEASUREMENTS ASSOCIATED WITH COVID-19.

Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose a flexible nonparametric mixed-effects mixture model that identifies risk factors and classifies patients with biological changes. We model the latent probability of biological changes using a logistic regression model and trajectories in the latent groups using smoothing splines. We developed an EM algorithm to maximize the penalized likelihood for estimating all parameters and mean functions. We evaluate our methods by simulations and apply the proposed model to investigate changes in temperature in a cohort of COVID-19-infected hemodialysis patients.