Semiparametric multivariate joint model for skewed-longitudinal and survival data: A Bayesian approach.

Joint models and statistical inference for longitudinal and survival data have been an active area of statistical research and have mostly coupled a longitudinal biomarker-based mixed-effects model with normal distribution and an event time-based survival model. In practice, however, the following issues may standout: (i) Normality of model error in longitudinal models is a routine assumption, but it may be unrealistically violating data features of subject variations. (ii) Data collected are often featured by the mixed types of multiple longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation. Additionally, a parametric model specification may be inflexible to capture the complicated patterns of longitudinal data. (iii) Missing observations in the longitudinal data are often encountered; the missing measures are likely to be informative (nonignorable) and ignoring this phenomenon may result in inaccurate inference. Multilevel item response theory (MLIRT) models have been increasingly used to analyze the multiple longitudinal data of mixed types (ie, continuous and categorical) in clinical studies. In this article, we develop an MLIRT-based semiparametric joint model with skew-t distribution that consists of an extended MLIRT model for the mixed types of multiple longitudinal data and a Cox proportional hazards model, linked through random-effects. A Bayesian approach is employed for joint modeling. Simulation studies are conducted to assess performance of the proposed models and method. A real example from primary biliary cirrhosis clinical study is analyzed to estimate parameters in the joint model and also evaluate sensitivity of parameter estimates for various plausible nonignorable missing data mechanisms.

Quantile-Adaptive Sufficient Variable Screening by Controlling False Discovery.

Sufficient variable screening rapidly reduces dimensionality with high probability in ultra-high dimensional modeling. To rapidly screen out the null predictors, a quantile-adaptive sufficient variable screening framework is developed by controlling the false discovery. Without any specification of an actual model, we first introduce a compound testing procedure based on the conditionally imputing marginal rank correlation at different quantile levels of response to select active predictors in high dimensionality. The testing statistic can capture sufficient dependence through two paths: one is to control false discovery adaptively and the other is to control the false discovery rate by giving a prespecified threshold. It is computationally efficient and easy to implement. We establish the theoretical properties under mild conditions. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.

Therapies, Research Funding, and Racial Diversity in Essential Tremor: A Systematic Review of the Literature.

Background: Essential tremor (ET) is one of the most common tremor disorders in the world. Despite this, only one medication, propranolol, is approved by the Food and Drug Administration to treat it. Objectives: We analyzed controlled clinical trials in ET, spanning the last 50 years, to identify potential shortcomings in the therapeutic clinical pipeline. Methods: Outcomes reviewed included demographics (specifically gender and race), therapeutic modalities, funding information, location of research, and trends over time. Clinical trials published in English were identified in scientific databases (Pubmed, SCOPUS, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform from 1970 through December 2021. Included trials were prospective, either single- or double-blinded (including blinded video assessments for surgical trials), with change in limb, head, or voice tremor as the primary outcome measure. Results: One hundred and eighty-six controlled clinical trials were accepted for extraction, including 4207 patients. Of the 145 trials that included gender, males comprised 59% of the patient population. Only 6.4% of studies provided racial demographics; in these studies, 70.5% of patients were Caucasian. The most common therapeutic modality over the past 50 years was "pharmaceutical" (56%), and the most common pharmaceutical studied was propranolol (32%). 41% of clinical trials reported no specific funding. Conclusions: Future efforts should focus on increasing funding for clinical trial research in ET worldwide, and trials should be designed to be more inclusive of disadvantaged minorities.

Multivariate piecewise joint models with random change-points for skewed-longitudinal and survival data.

Methodological development and application of joint models for longitudinal and time-to-event data have mostly coupled a single longitudinal outcome-based linear mixed-effects model with normal distribution and Cox proportional hazards model. In practice, however, (i) profile of subject's longitudinal response may follow a `broken-stick nonlinear' (piecewise) trajectory. Such multiple phases are an important indicator to help quantify treatment effect, disease diagnosis and clinical decision-making. (ii) Normality in longitudinal models is a routine assumption, but it may be unrealistically obscuring important features of subject variations. (iii) Data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation. (iv) It is of importance to investigate how multivariate longitudinal outcomes are associated with event time of interest. In the article, driven by a motivating example, we propose Bayesian multivariate piecewise joint models with a skewed distribution and random change-points for longitudinal measures with an attempt to cope with correlated multivariate longitudinal data, adjust departures from normality, mediate accuracy from longitudinal trajectories with random change-point and tailor linkage in specifying a time-to-event process. A real example is analyzed to demonstrate methodology and simulation studies are conducted to evaluate performance of the proposed models and method.

Bayesian Joint Modeling of Multivariate Longitudinal and Survival Data With an Application to Diabetes Study.

Joint models of longitudinal and time-to-event data have received a lot of attention in epidemiological and clinical research under a linear mixed-effects model with the normal assumption for a single longitudinal outcome and Cox proportional hazards model. However, those model-based analyses may not provide robust inference when longitudinal measurements exhibit skewness and/or heavy tails. In addition, the data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, and ignoring their correlation may lead to biased estimation. Under the umbrella of Bayesian inference, this article introduces multivariate joint (MVJ) models with a skewed distribution for multiple longitudinal exposures in an attempt to cope with correlated multiple longitudinal outcomes, adjust departures from normality, and tailor linkage in specifying a time-to-event process. We develop a Bayesian joint modeling approach to MVJ models that couples a multivariate linear mixed-effects (MLME) model with the skew-normal (SN) distribution and a Cox proportional hazards model. Our proposed models and method are evaluated by simulation studies and are applied to a real example from a diabetes study.

Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation.

Artificial Intelligence (AI)-based methods allow for automatic assessment of pain intensity based on continuous monitoring and processing of subtle changes in sensory signals, including facial expression, body movements, and crying frequency. Currently, there is a large and growing need for expanding current AI-based approaches to the assessment of postoperative pain in the neonatal intensive care unit (NICU). In contrast to acute procedural pain in the clinic, the NICU has neonates emerging from postoperative sedation, usually intubated, and with variable energy reserves for manifesting forceful pain responses. Here, we present a novel multi-modal approach designed, developed, and validated for assessment of neonatal postoperative pain in the challenging NICU setting. Our approach includes a robust network capable of efficient reconstruction of missing modalities (e.g., obscured facial expression due to intubation) using an unsupervised spatio-temporal feature learning with a generative model for learning the joint features. Our approach generates the final pain score along with the intensity using an attentional cross-modal feature fusion. Using experimental dataset from postoperative neonates in the NICU, our pain assessment approach achieves superior performance (AUC 0.906, accuracy 0.820) as compared to the state-of-the-art approaches.

Hyperosmolarity Deserves More Attention in Critically Ill COVID-19 Patients with Diabetes: A Cohort-Based Study.

PURPOSE: Recently, a cluster of pneumonia caused by SARS-CoV-2 were identified in Wuhan and spread throughout the world. More information about risk factors for mortality of critically ill patients infected with SARS-CoV-2 remain to be evaluated. METHODS: We included adult patients confirmed with SARS-CoV-2 infection who were critically ill and admitted to the intensive care unit (ICU) of Tongji Hospital in Wuhan from Feb 4, 2020 to Feb 20, 2020. Data were collected and compared between patients who died and improved. Logistic regression was used to explore the risk factors for death of SARS-CoV-2-infected critically ill patients. RESULTS: A total of 160 critically ill patients with SARS-CoV-2 infection were included, of which 146 patients with appeared outcomes were included into the final analysis. The random blood glucose, serum sodium and effective plasma osmolarity were higher in deceased patients, especially in patients with diabetes. There were 7 patients with diabetes with hyperosmolar status and all of them were deceased. Multivariable regression revealed that older age (odds ratio 4.28, 95% CI 1.01-18.20; p = 0.049), higher C-reactive protein (odds ratio 1.01, 1.00-1.03; p = 0.024), higher interleukin-6 (odds ratio 1.01, 1.00-1.03; p = 0.0323), and d-dimer greater than 1 µg/mL (odds ratio 1.10, 1.01-1.20; p = 0.032) at admission were associated with increased odds of death. CONCLUSION: In conclusion, hyperosmolarity needs more attention and may contribute to mortality in critically ill patients with COVID-19, especially in those with diabetes. Older age, inflammatory response, and thrombosis may be risk factors for death of critically ill patients with SARS-CoV-2 infection.

Bayesian joint modeling for partially linear mixed-effects quantile regression of longitudinal and time-to-event data with limit of detection, covariate measurement errors and skewness.

Joint modeling analysis of longitudinal and time-to-event data has been an active area of statistical methodological study and biomedical research, but the majority of them are based on mean-regression. Quantile regression (QR) can characterize the entire conditional distribution of the outcome variable, and may be more robust to outliers/heavy tails and misspecification of error distribution. Additionally, a parametric specification may be insufficient and inflexible to capture the complicated longitudinal pattern of biomarkers. Thus, this study proposes novel QR-based partially linear mixed-effects joint models with three components (QR-based longitudinal response, longitudinal covariate, and time-to-event processes), and applies to Multicenter AIDS Cohort Study (MACS). Many common data features, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution, are considered to obtain reliable parameter estimates. Many interesting findings are discovered by the complicated joint models under Bayesian inference framework. Simulation studies are also implemented to assess the performance of the proposed joint models under different scenarios.

Estimation of the Outbreak Severity and Evaluation of Epidemic Prevention Ability of COVID-19 by Province in China.

Objectives. To compare the epidemic prevention ability of COVID-19 of each province in China and to evaluate the existing prevention and control capacity of each province.Methods. We established a quasi-Poisson linear mixed-effects model using the case data in cities outside Wuhan in Hubei Province, China. We adapted this model to estimate the number of potential cases in Wuhan and obtained epidemiological parameters. We estimated the initial number of cases in each province by using passenger flowrate data and constructed the extended susceptible-exposed-infectious-recovered model to predict the future disease transmission trends.Results. The estimated potential cases in Wuhan were about 3 times the reported cases. The basic reproductive number was 3.30 during the initial outbreak. Provinces with more estimated imported cases than reported cases were those in the surrounding provinces of Hubei, including Henan and Shaanxi. The regions where the number of reported cases was closer to the predicted value were most the developed areas, including Beijing and Shanghai.Conclusions. The number of confirmed cases in Wuhan was underestimated in the initial period of the outbreak. Provincial surveillance and emergency response capabilities vary across the country.

Distinct Growth Phases in Early Life Associated With the Risk of Type 1 Diabetes: The TEDDY Study.

OBJECTIVE: This study investigates two-phase growth patterns in early life and their association with development of islet autoimmunity (IA) and type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: The Environmental Determinants of Diabetes in the Young (TEDDY) study followed 7,522 genetically high-risk children in Sweden, Finland, Germany, and the U.S. from birth for a median of 9.0 years (interquartile range 5.7-10.6) with available growth data. Of these, 761 (10.1%) children developed IA and 290 (3.9%) children were diagnosed with T1D. Bayesian two-phase piecewise linear mixed models with a random change point were used to estimate children's individual growth trajectories. Cox proportional hazards models were used to assess the effects of associated growth parameters on the risks of IA and progression to T1D. RESULTS: A higher rate of weight gain in infancy was associated with increased IA risk (hazard ratio [HR] 1.09 [95% CI 1.02, 1.17] per 1 kg/year). A height growth pattern with a lower rate in infancy (HR 0.79 [95% CI 0.70, 0.90] per 1 cm/year), higher rate in early childhood (HR 1.48 [95% CI 1.22, 1.79] per 1 cm/year), and younger age at the phase transition (HR 0.76 [95% CI 0.58, 0.99] per 1 month) was associated with increased risk of progression from IA to T1D. A higher rate of weight gain in early childhood was associated with increased risk of progression from IA to T1D (HR 2.57 [95% CI 1.34, 4.91] per 1 kg/year) in children with first-appearing GAD autoantibody only. CONCLUSIONS: Growth patterns in early life better clarify how specific growth phases are associated with the development of T1D.

Quantile regression-based Bayesian joint modeling analysis of longitudinal-survival data, with application to an AIDS cohort study.

In longitudinal studies, it is of interest to investigate how repeatedly measured markers are associated with time to an event. Joint models have received increasing attention on analyzing such complex longitudinal-survival data with multiple data features, but most of them are mean regression-based models. This paper formulates a quantile regression (QR) based joint models in general forms that consider left-censoring due to the limit of detection, covariates with measurement errors and skewness. The joint models consist of three components: (i) QR-based nonlinear mixed-effects Tobit model using asymmetric Laplace distribution for response dynamic process; (ii) nonparametric linear mixed-effects model with skew-normal distribution for mismeasured covariate; and (iii) Cox proportional hazard model for event time. For the purpose of simultaneously estimating model parameters, we propose a Bayesian method to jointly model the three components which are linked through the random effects. We apply the proposed modeling procedure to analyze the Multicenter AIDS Cohort Study data, and assess the performance of the proposed models and method through simulation studies. The findings suggest that our QR-based joint models may provide comprehensive understanding of heterogeneous outcome trajectories at different quantiles, and more reliable and robust results if the data exhibits these features.

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

A Behavioral Physical Activity Intervention to Manage Moderate and Severe Fatigue Among Head and Neck Cancer Patients-Pre-efficacy Study in the National Institutes of Health ORBIT Model.

BACKGROUND: Cancer-related fatigue (CRF) reduces head and neck cancer (HNC) survival rates and is the most common, severe, and distressing symptom negatively impacting activities of daily living (ADLs) dependence among HNC patients. These patients remain physically inactive after their cancer treatment, although there is consensus that physical activity mitigates CRF in cancer patients. OBJECTIVE: A home-based personalized behavioral physical activity intervention with fitness graded motion exergames (PAfitME) was evaluated for its intervention components, intervention delivery mode, and intervention contact time/duration with initial assessment of the feasibility, acceptability, safety, and outcomes. METHODS: This study (N = 8) was a single-group, pre-post design to evaluate a 6-week PAfitME at the end of HNC treatment. Health outcomes were CRF, ADL dependence, and fitness performance. Behavioral outcomes were exergame adherence. RESULTS: Positive health and behavioral outcomes support the PAfitME protocol including intervention components, intervention delivery mode, and intervention contact times/duration. The PAfitME intervention is feasible and acceptable with promising adherence rates. No adverse events were reported. There was marked improvement in CRF, ADL dependence, cardiorespiratory fitness, balance, muscle strength, and shoulder forward flexion, with large to moderate effect sizes as a result of the PAfitME intervention. CONCLUSION: The PAfitME protocol is ready for additional testing in a randomized clinical trial. IMPLICATIONS FOR PRACTICE: The PAfitME intervention is a nurse-led nonpharmacological intervention. It can be integrated into home care or telehealth care for HNC patients at the end of their cancer treatment once effectiveness is established.

Double-blind, randomized and controlled trial of EPI-743 in Friedreich's ataxia.

AIM: To evaluate the safety and clinical effects of EPI-743 in Friedreich's ataxia patients. EPI-743 is a compound that targets oxidoreductase enzymes essential for redox control of metabolism. METHODS: We conducted a multicenter trial that evaluated EPI-743 during a 6-month placebo-controlled phase, followed by an 18-month open-label phase. End points included low-contrast visual acuity and the Friedreich's Ataxia Rating Scale. RESULTS/CONCLUSION: EPI-743 was demonstrated to be safe and well tolerated. There were no significant improvements in key end points during the placebo phase. However, at 24 months, EPI-743 treatment was associated with a statistically significant improvement in neurological function and disease progression relative to a natural history cohort (p < 0.001).

Recurrence of Genital Infections With 9 Human Papillomavirus (HPV) Vaccine Types (6, 11, 16, 18, 31, 33, 45, 52, and 58) Among Men in the HPV Infection in Men (HIM) Study.

Background: The purpose of this study was to assess genital recurrence of human papillomavirus (HPV) genotypes included in the 9-valent vaccine and to investigate factors associated with recurrence among men in the HPV Infection in Men (HIM) Study. Methods: Men were followed every 6 months for a median of 3.7 years. HPV genotypes were detected using Roche linear array. Factors associated with type-specific HPV recurrence (infections occurring after a ≥12-month infection-free period) were assessed. Results: In type-specific analyses, 31% of prior prevalent and 20% of prior incident infections recurred. Among prevalent infections, HPV types 52, 45, 16, 58, and 6 and among incident infections, HPV types 58, 52, 18, 16, and 11 had the highest rates of recurrence. New sexual partners (male or female) and frequency of sexual intercourse with female partners were associated with HPV-6, -16, -31, and -58 infection recurrence. In grouped analyses, lifetime and new male sexual partners were associated with recurrence of prior incident infection with any of the 9 HPV types. Conclusions: Recurrence of genital HPV infections is relatively common among men and associated with high-risk sexual behavior. Further studies are needed to understand the role of HPV recurrence in the etiology of HPV-associated diseases.

Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies.

Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.

Bayesian joint analysis of heterogeneous- and skewed-longitudinal data and a binary outcome, with application to AIDS clinical studies.

In medical studies, heterogeneous- and skewed-longitudinal data with mis-measured covariates are often observed together with a clinically important binary outcome. A finite mixture of joint models is currently used to fit heterogeneous-longitudinal data and binary outcome, in which these two parts are connected by the individual latent class membership. The skew distributions, such as skew-normal and skew-t, have shown beneficial in dealing with asymmetric data in various applications in literature. However, there has been relatively few studies concerning joint modeling of heterogeneous- and skewed-longitudinal data and a binary outcome. In this article, we propose a joint model in which a flexible finite mixture of nonlinear mixed-effects models with skew distributions is connected with binary logistic model by a latent class membership indicator. Simulation studies are conducted to assess the performance of the proposed models and method, and a real example from an AIDS clinical trial study illustrates the methodology by modeling the viral dynamics to compare potential models with different distribution specifications; the analysis results are reported.

Longitudinal gait and balance decline in Friedreich's Ataxia: A pilot study.

INTRODUCTION: Friedreich's Ataxia (FA) is a devastating, progressive, neurodegenerative disease. Objective measures that detect changes in neurological function in FA patients are needed to facilitate therapeutic clinical trials. The purpose of this pilot study was to analyze longitudinal changes in gait and balance in subjects with FA using the GAITRite Walkway System® and Biodex Balance System™, respectively, and to test the ability of these measures to detect change over time compared to the Friedreich's Ataxia Rating Scale (FARS). METHODS: This was a 24-month longitudinal study comparing ambulatory FA subjects with age- and gender-matched, healthy controls. Eight FA subjects and 8 controls were tested at regular intervals using the GAITRite and Biodex Balance systems and the FARS. RESULTS: In the FA group, comfortable and fast gait velocity declined 8.0% and 13.9% after 12 months and 24.1% and 30.3% after 24 months, respectively. Postural stability indices increased in FA subjects an average of 41% from baseline to 24 months, representing a decline in balance. Subjects with FA also demonstrated a 17.7% increase in FARS neurological exam scores over 24 months. There were no changes in gait or balance variables in controls. In the FA group, multiple gait and balance measures correlated significantly with FARS neurological exam scores. CONCLUSIONS: The GAITRite and Biodex Balance systems provided objective and clinically relevant measures of functional decline in subjects with FA that correlated significantly with performance measures in the FARS. Gait velocity may be an important objective measure to identify disease progression in adults with FA.

Generalizability analysis for clinical trials: a simulation study.

Subjects are rarely selected on a random basis from a well-defined patient population of interest into a clinical trial, with women, children, the elderly, and those with common comorbidities who are frequently underrepresented. Decades of clinical experience have demonstrated that the application of trial findings to individual patients is permissible by using efficacy as a measure of effectiveness and assuming that the characteristics of patients are sufficiently similar. In order to investigate this issue in greater depth, we simulated a patient population with treatment effect size of 0.5 (Cohen's d) and five covariates that included gender, health insurance, comorbidity, age, and motivation. To demonstrate how selection of patients for a clinical trial can bias the results when treatment effect varies across individuals, we created 50 nonrandom clinical trials based on this patient population and showed relative bias to range from 1.68% to 99.70%. We calculated and evaluated three indexes: C-statistics, standardized mean difference (SMD), and Tipton's index (ß) of generalization for the 50 nonrandom trials. Findings indicated that (i) the ranges were 0.56-0.98, 0.23-11.17, and 0.99-0.73 for C-statistics, SMD, and ß, respectively, when treatment effect bias increased from 1.68% to 99.70% and (ii) C-statistics < 0.86, SMD < 1.95, and ß > 0.91 when treatment effect bias <50%. Recommendations are made using existing generalization indexes on the basis of our simulation results. An example from a real clinical trial is provided for illustration. Copyright © 2017 John Wiley & Sons, Ltd.