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

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.

Bayesian blockwise inference for joint models of longitudinal and multistate data with application to longitudinal multimorbidity analysis.

Multistate models provide a useful framework for modelling complex event history data in clinical settings and have recently been extended to the joint modelling framework to appropriately handle endogenous longitudinal covariates, such as repeatedly measured biomarkers, which are informative about health status and disease progression. However, the practical application of such joint models faces considerable computational challenges. Motivated by a longitudinal multimorbidity analysis of large-scale UK health records, we introduce novel Bayesian inference approaches for these models that are capable of handling complex multistate processes and large datasets with straightforward implementation. These approaches decompose the original estimation task into smaller inference blocks, leveraging parallel computing and facilitating flexible model specification and comparison. Using extensive simulation studies, we show that the proposed approaches achieve satisfactory estimation accuracy, with notable gains in computational efficiency compared to the standard Bayesian estimation strategy. We illustrate our approaches by analysing the coevolution of routinely measured systolic blood pressure and the progression of three important chronic conditions, using a large dataset from the Clinical Practice Research Datalink Aurum database. Our analysis reveals distinct and previously lesser-known association structures between systolic blood pressure and different disease transitions.

Joint modeling of longitudinal and survival data for the evaluation of the impact of irrigation systems on the time until the occurrence of phoma leaf spot in Arabica coffee plants.

BACKGROUND: The coffee crop is prominent in Brazilian agriculture, making the country a global power in this area. One of the main concerns in the coffee sector is disease, which can affect coffee productivity and quality. Thus, it is important to evaluate the factors that may affect coffee quality and thus enhance the development of strategies to reduce coffee losses and costs and optimize production. This study evaluated the influence of the type of irrigation (self-propelled, drip, and center pivot) on the time until the occurrence of phoma leaf spot on Arabica coffee plants, considering the intensity of the disease. Additionally, the association between longitudinal incidence and the time until an event of interest was assessed based on the joint modeling of longitudinal and survival data. RESULTS: The results of this study identify the effectiveness of drip irrigation system compared with other systems; the use of such systems was associated with an ~46.5% reduction in the risk of leaf spot disease compared with the use of a self-propelled irrigation system. The use of a center pivot system increased the risk of disease progression compared with a self-propelled system. An association between the longitudinal and survival processes was also observed. CONCLUSION: The findings demonstrate the superior performance of the drip irrigation system in controlling phoma leaf spot disease in Arabica coffee plants compared with self-propelled and center pivot systems. This research highlights the potential of using drip irrigation to establish more effective agricultural practices in coffee cultivation, contributing to better disease management and improved crop quality. © 2024 Society of Chemical Industry.

Joint modeling of longitudinal health-related quality of life during concurrent chemoradiotherapy period and long-term survival among patients with advanced nasopharyngeal carcinoma.

BACKGROUND: To investigate the prognosis of longitudinal health-related quality of life (HRQOL) during concurrent chemoradiotherapy (CCRT) on survival outcomes in patients with advanced nasopharyngeal carcinoma (NPC). METHODS: During 2012-2014, 145 adult NPC patients with stage II-IVb NPC were investigated weekly using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire core 30 (EORCT QLQ-C30) during their CCRT period. The effects of longitudinal trends of HRQOL on survival outcomes were estimated using joint modeling, and hazard ratios (HRs) with 95% confidence intervals (95% CIs) were reported as a 10-point increase in HRQOL scores. RESULTS: After a median follow-up of 83.4 months, the multivariable models showed significant associations of longitudinal increasing scores in fatigue and appetite loss during the CCRT period with distant metastasis-free survival: 10-point increases in scores of fatigue and appetite loss domains during CCRT period were significantly associated with 75% (HR: 1.75, 95% CI: 1.01, 3.02; p = 0.047) and 59% (HR: 1.59, 95% CI: 1.09, 2.59; p = 0.018) increase in the risk of distant metastasis, respectively. The prognostic effects of the longitudinal HRQOL trend on overall survival and progress-free survival were statistically non-significant. CONCLUSION: Increases in fatigue and appetite loss of HRQOL during the CCRT period are significantly associated with high risks of distant metastasis in advanced NPC patients. Nutritional support and psychological intervention are warranted for NPC patients during the treatment period.

Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it intends to evaluate and compare the predictive performance of a joint model incorporating four types of features with standalone 2.5D deep learning, radiomics, radiology, and clinical models for early expansion in sICH. A total of 254 sICH patients were enrolled retrospectively and divided into two groups according to whether the hematoma was enlarged or not. The 2.5D mask of sICH is constructed with the maximum axial, coronal and sagittal planes of the hematoma, which is used to train the deep learning model and extract deep learning features. Predictive models were built on clinic, radiology, radiomics and deep learning features separately and four type features jointly. The diagnostic performance of each model was measured using the receiver operating characteristic curve (AUC), Accuracy, Recall, F1 and decision curve analysis (DCA). The AUCs of the clinic model, radiology model, radiomics model, deep learning model, joint model, and nomogram model on the train set (training and Cross-validation) were 0.639, 0.682, 0.859, 0.807, 0.939, and 0.942, respectively, while the AUCs on the test set (external validation) were 0.680, 0.758, 0.802, 0.857, 0.929, and 0.926. Decision curve analysis showed that the joint model was superior to the other models and demonstrated good consistency between the predicted probability of early hematoma expansion and the actual occurrence probability. Our study demonstrates that the joint model is a more efficient and robust prediction model, as verified by multicenter data. This finding highlights the potential clinical utility of a multifactorial prediction model that integrates various data sources for prognostication in patients with intracerebral hemorrhage. The Critical Relevance Statement: Combining 2.5D deep learning features with clinic features, radiology markers, and radiomics signatures to establish a joint model enabling physicians to conduct better-individualized assessments the risk of early expansion of sICH.

Trivariate Joint Modeling for Family Data with Longitudinal Counts, Recurrent Events and a Terminal Event with Application to Lynch Syndrome.

Trivariate joint modeling for longitudinal count data, recurrent events, and a terminal event for family data has increased interest in medical studies. For example, families with Lynch syndrome (LS) are at high risk of developing colorectal cancer (CRC), where the number of polyps and the frequency of colonoscopy screening visits are highly associated with the risk of CRC among individuals and families. To assess how screening visits influence polyp detection, which in turn influences time to CRC, we propose a clustered trivariate joint model. The proposed model facilitates longitudinal count data that are zero-inflated and over-dispersed and invokes individual-specific and family-specific random effects to account for dependence among individuals and families. We formulate our proposed model as a latent Gaussian model to use the Bayesian estimation approach with the integrated nested Laplace approximation algorithm and evaluate its performance using simulation studies. Our trivariate joint model is applied to a series of 18 families from Newfoundland, with the occurrence of CRC taken as the terminal event, the colonoscopy screening visits as recurrent events, and the number of polyps detected at each visit as zero-inflated count data with overdispersion. We showed that our trivariate model fits better than alternative bivariate models and that the cluster effects should not be ignored when analyzing family data. Finally, the proposed model enables us to quantify heterogeneity across families and individuals in polyp detection and CRC risk, thus helping to identify individuals and families who would benefit from more intensive screening visits.

Anti-Spike IgG antibodies as correlates of protection against SARS-CoV-2 infection in the pre-Omicron and Omicron era.

Anti-Spike IgG antibodies against SARS-CoV-2, which are elicited by vaccination and infection, are correlates of protection against infection with pre-Omicron variants. Whether this association can be generalized to infections with Omicron variants is unclear. We conducted a retrospective cohort study with 8457 blood donors in Tyrol, Austria, analyzing 15,340 anti-Spike IgG antibody measurements from March 2021 to December 2022 assessed by Abbott SARS-CoV-2 IgG II chemiluminescent microparticle immunoassay. Using a Bayesian joint model, we estimated antibody trajectories and adjusted hazard ratios for incident SARS-CoV-2 infection ascertained by self-report or seroconversion of anti-Nucleocapsid antibodies. At the time of their earliest available anti-Spike IgG antibody measurement (median November 23, 2021), participants had a median age of 46.0 years (IQR 32.8-55.2), with 45.3% being female, 41.3% having a prior SARS-CoV-2 infection, and 75.5% having received at least one dose of a COVID-19 vaccine. Among 6159 participants with endpoint data, 3700 incident SARS-CoV-2 infections with predominantly Omicron sublineages were recorded over a median of 8.8 months (IQR 5.7-12.4). The age- and sex-adjusted hazard ratio for SARS-CoV-2 associated with having twice the anti-Spike IgG antibody titer was 0.875 (95% credible interval 0.868-0.881) overall, 0.842 (0.827-0.856) during 2021, and 0.884 (0.877-0.891) during 2022 (all p < 0.001). The associations were similar in females and males (Pinteraction = 0.673) and across age (Pinteraction = 0.590). Higher anti-Spike IgG antibody titers were associated with reduced risk of incident SARS-CoV-2 infection across the entire observation period. While the magnitude of association was slightly weakened in the Omicron era, anti-Spike IgG antibody continues to be a suitable correlate of protection against newer SARS-CoV-2 variants.

Factor-augmented transformation models for interval-censored failure time data.

Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.

Relationship between prostate-specific antigen, alkaline phosphatase levels, and time-to-tumor shrinkage: understanding the progression of prostate cancer in a longitudinal study.

BACKGROUND: This study delves into the complex interplay among prostate-specific antigen, alkaline phosphatase, and the temporal dynamics of tumor shrinkage in prostate cancer. By investigating the longitudinal trajectories and time-to-prostate cancer tumor shrinkage, we aim to untangle the intricate patterns of these biomarkers. This understanding is pivotal for gaining profound insights into the multifaceted aspects of prostate cancer progression. The joint model approach serves as a comprehensive framework, facilitating the elucidation of intricate interactions among these pivotal elements within the context of prostate cancer . METHODS: A new joint model under a shared parameters strategy is proposed for mixed bivariate longitudinal biomarkers and event time data, for obtaining accurate estimates in the presence of missing covariate data. The primary innovation of our model resides in its effective management of covariates with missing observations. Built upon established frameworks, our joint model extends its capabilities by integrating mixed longitudinal responses and accounting for missingness in covariates, thus confronting this particular challenge. We posit that these enhancements bolster the model's utility and dependability in real-world contexts characterized by prevalent missing data. The main objective of this research is to provide a model-based approach to get full information from prostate cancer data collected with patients' baseline characteristics ( Age , body mass index ( BMI ), GleasonScore , Grade , and Drug ) and two longitudinal endogenous covariates ( Platelets and Bilirubin ). RESULTS: The results reveal a clear association between prostate-specific antigen and alkaline phosphatase biomarkers in the context of time-to-prostate cancer tumor shrinkage. This underscores the interconnected dynamics of these key indicators in gauging disease progression. CONCLUSIONS: The analysis of the prostate cancer dataset, incorporating a joint evaluation of mixed longitudinal prostate-specific antigen and alkaline phosphatase biomarkers alongside tumor status, has provided valuable insights into disease progression. The results demonstrate the effectiveness of the proposed joint model, as evidenced by accurate estimates. The shared variables associated with both longitudinal biomarkers and event times consistently deviate from zero, highlighting the robustness and reliability of the model in capturing the complex dynamics of prostate cancer progression. This approach holds promise for enhancing our understanding and predictive capabilities in the clinical assessment of prostate cancer.

Individualized survival predictions using state space model with longitudinal and survival data.

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two-stage method.

Ecological momentary assessment (EMA), a data collection method commonly employed in mHealth studies, allows for repeated real-time sampling of individuals' psychological, behavioral, and contextual states. Due to the frequent measurements, data collected using EMA are useful for understanding both the temporal dynamics in individuals' states and how these states relate to adverse health events. Motivated by data from a smoking cessation study, we propose a joint model for analyzing longitudinal EMA data to determine whether certain latent psychological states are associated with repeated cigarette use. Our method consists of a longitudinal submodel-a dynamic factor model-that models changes in the time-varying latent states and a cumulative risk submodel-a Poisson regression model-that connects the latent states with the total number of events. In the motivating data, both the predictors-the underlying psychological states-and the event outcome-the number of cigarettes smoked-are partially unobservable; we account for this incomplete information in our proposed model and estimation method. We take a two-stage approach to estimation that leverages existing software and uses importance sampling-based weights to reduce potential bias. We demonstrate that these weights are effective at reducing bias in the cumulative risk submodel parameters via simulation. We apply our method to a subset of data from a smoking cessation study to assess the association between psychological state and cigarette smoking. The analysis shows that above-average intensities of negative mood are associated with increased cigarette use.

A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data.

In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.

Determinants of hemoglobin level and time to default from Highly Active Antiretroviral Therapy (HAART) for adult clients living with HIV under treatment; a retrospective cohort study design.

HIV/AIDS is one of the most devastating infectious diseases affecting humankind all over the world and its impact goes beyond public health problems. This study was conducted to investigate the joint predictors of hemoglobin level and time to default from treatment for adult clients living with HIV/AIDS under HAART at the University of Gondar Comprehensive and Specialized Hospital, North-west Ethiopia. The study was conducted using a retrospective cohort design from the medical records of 403 randomly selected adult clients living with HIV whose follow-ups were from September 2015 to March 2022. Hemoglobin level was projected using Sahli's acid-hematin method. Hence, the hemoglobin tube was filled with N/10 hydrochloric acid up to 2 g % marking and the graduated tube was placed in Sahli's hemoglobin meter. The blood samples were collected using the finger-pick method, considering 22 G disposable needles. The health staff did this. From a total of 403 adult patients living with HIV/AIDS included in the current study, about 44.2% defaulted from therapy. The overall mean and median estimated survival time of adult clients under study were 44.3 and 42 months respectively. The patient's lymphocyte count (AHR = 0.7498, 95% CI: (0.7411: 0.7587), p-value < 0.01), The weight of adult patients living with HIV/AIDS (AHR = 0.9741, 95% CI: (0.9736: 0.9747), p-value = 0.012), sex of adult clients (AHR = 0.6019, 95% CI: (0.5979, 0.6059), p-value < 0.01), WHO stages III compared to Stage I (AHR = 1.4073, 95% CI: (1.3262, 1.5078), p-value < 0.01), poor adherence level (AHR = 0.2796, 95% CI: (0.2082, 0.3705) and p-value < 0.01), bedridden patients (AHR = 1.5346, 95% CI: (1.4199, 1.6495), p-value = 0.008), and opportunistic infections (AHR = 0.2237, 95% CI: (0.0248, 0.4740), p-value = 0.004) had significant effect on both hemoglobin level and time to default from treatment. Similarly, other co-morbidity conditions, disclosure status of the HIV disease, and tobacco and alcohol addiction had a significant effect on the variables of interest. The estimate of the association parameter in the slope value of Hgb level and time default was negative, indicating that the Hgb level increased as the hazard of defaulting from treatment decreased. A patient with abnormal BMI like underweight, overweight, or obese was negatively associated with the risk of anemia (lower hemoglobin level). As a recommendation, more attention should be given to those patients with abnormal BMI, patients with other co-morbidity conditions, patients with opportunistic infections, and low lymphocytes, and bedridden and ambulatory patients. Health-related education should be given to adult clients living with HIV/AIDS to be good adherents for medical treatment.

A joint model for lesion segmentation and classification of MS and NMOSD.

Introduction: Multiple sclerosis (MS) and neuromyelitis optic spectrum disorder (NMOSD) are mimic autoimmune diseases of the central nervous system with a very high disability rate. Their clinical symptoms and imaging findings are similar, making it difficult to diagnose and differentiate. Existing research typically employs the T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI imaging technique to focus on a single task in MS and NMOSD lesion segmentation or disease classification, while ignoring the collaboration between the tasks. Methods: To make full use of the correlation between lesion segmentation and disease classification tasks of MS and NMOSD, so as to improve the accuracy and speed of the recognition and diagnosis of MS and NMOSD, a joint model is proposed in this study. The joint model primarily comprises three components: an information-sharing subnetwork, a lesion segmentation subnetwork, and a disease classification subnetwork. Among them, the information-sharing subnetwork adopts a dualbranch structure composed of a convolution module and a Swin Transformer module to extract local and global features, respectively. These features are then input into the lesion segmentation subnetwork and disease classification subnetwork to obtain results for both tasks simultaneously. In addition, to further enhance the mutual guidance between the tasks, this study proposes two information interaction methods: a lesion guidance module and a crosstask loss function. Furthermore, the lesion location maps provide interpretability for the diagnosis process of the deep learning model. Results: The joint model achieved a Dice similarity coefficient (DSC) of 74.87% on the lesion segmentation task and accuracy (ACC) of 92.36% on the disease classification task, demonstrating its superior performance. By setting up ablation experiments, the effectiveness of information sharing and interaction between tasks is verified. Discussion: The results show that the joint model can effectively improve the performance of the two tasks.

Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods.

Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.

The effect of obesity phenotype changes on cardiovascular outcomes in adults older than 40 years in the prospective cohort of the Tehran lipids and glucose study (TLGS): joint model of longitudinal and time-to-event data.

BACKGROUND: Obesity is a worldwide health concern with serious clinical effects, including myocardial infarction (MI), stroke, cardiovascular diseases (CVDs), and all-cause mortality. The present study aimed to assess the association of obesity phenotypes and different CVDs and mortality in males and females by simultaneously considering the longitudinal and survival time data. METHODS: In the Tehran Lipid and Glucose Study (TLGS), participants older than three years were selected by a multi-stage random cluster sampling method and followed for about 19 years. In the current study, individuals aged over 40 years without a medical history of CVD, stroke, MI, and coronary heart disease were included. Exclusions comprised those undergoing treatment for CVD and those with more than 30% missing information or incomplete data. Joint modeling of longitudinal binary outcome and survival time data was applied to assess the dependency and the association between the changes in obesity phenotypes and time to occurrence of CVD, MI, stroke, and CVD mortality. To account for any potential sex-related confounding effect on the association between the obesity phenotypes and CVD outcomes, sex-specific analysis was carried out. The analysis was performed using packages (JMbayes2) of R software (version 4.2.1). RESULTS: Overall, 6350 adults above 40 years were included. In the joint modeling of CVD outcome among males, literates and participants with a family history of diabetes were at lower risk of CVD compared to illiterates and those with no family history of diabetes in the Bayesian Cox model. Current smokers were at higher risk of CVD compared to non-smokers. In a logistic mixed effects model, odds of obesity phenotype was higher among participants with low physical activity, family history of diabetes and older age compared to males with high physical activity, no family history of diabetes and younger age. In females, based on the results of the Bayesian Cox model, participants with family history of diabetes, family history of CVD, abnormal obesity phenotype and past smokers had a higher risk of CVD compared to those with no history of diabetes, CVD and nonsmokers. In the obesity varying model, odds of obesity phenotype was higher among females with history of diabetes and older age compared to those with no history of diabetes and who were younger. There was no significant variable associated with MI among males in the Bayesian Cox model. Odds of obesity phenotype was higher in males with low physical activity compared to those with high physical activity in the obesity varying model, whereas current smokers were at lower odds of obesity phenotype than nonsmokers. In females, risk of MI was higher among those with family history of diabetes compared to those with no history of diabetes in the Bayesian Cox model. In the logistic mixed effects model, a direct and significant association was found between age and obesity phenotype. In males, participants with history of diabetes, abnormal obesity phenotype and older age were at higher risk of stroke in the Bayesian Cox model compared to males with no history of diabetes, normal obesity phenotype and younger persons. In the obesity varying model, odds of obesity phenotype was higher in males with low physical activity, family history of diabetes and older age compared to those with high physical activity, no family history of diabetes and who were younger. Smokers had a lower odds of obesity phenotype than nonsmokers. In females, past smokers and those with family history of diabetes were at higher risk of stroke compared to nonsmokers and females with no history of diabetes in the Bayesian Cox model. In the obesity varying model, females with family history of diabetes and older ages had a higher odds of obesity phenotype compared to those with no family history of diabetes and who were younger. Among males, risk of CVD mortality was lower in past smokers compared to nonsmokers in the survival model. A direct and significant association was found between age and CVD mortality. Odds of obesity phenotype was higher in males with a history of diabetes than in those with no family history of diabetes in the logistic mixed effects model. CONCLUSIONS: It seems that modifications to metabolic disorders may have an impact on the heightened incidence of CVDs. Based on this, males with obesity and any type of metabolic disorder had a higher risk of CVD, stroke and CVD mortality (excluding MI) compared to those with a normal body mass index (BMI) and no metabolic disorders. Females with obesity and any type of metabolic disorder were at higher risk of CVD(, MI and stroke compared to those with a normal BMI and no metabolic disorders suggesting that obesity and metabolic disorders are related. Due to its synergistic effect on high blood pressure, metabolic disorders raise the risk of CVD.

Joint modeling of longitudinal CD4 count data and time to first occurrence of composite outcome.

In this study, we jointly modeled longitudinal CD4 count data and survival outcome (time-to-first occurrence of composite outcome of death, cardiac tamponade or constriction) in other to investigate the effects of Mycobacterium indicus pranii immunotherapy and the CD4 count measurements on the hazard of the composite outcome among patients with HIV and tuberculous (TB) pericarditis. In this joint modeling framework, the models for longitudinal and the survival data are linked by an association structure. The association structure represents the hazard of the event for 1-unit increase in the longitudinal measurement. Models fitting and parameter estimation were carried out using R version 4.2.3. The association structure that represents the strength of the association between the hazard for an event at time point j and the area under the longitudinal trajectory up to the same time j provides the best fit. We found that 1-unit increase in CD4 count results in 2 % significant reduction in the hazard of the composite outcome. Among HIV and TB pericarditis individuals, the hazard of the composite outcome does not differ between of M.indicus pranii versus placebo. Application of joint models to investigate the effect of M.indicus pranii on the hazard of the composite outcome is limited. Hence, this study provides information on the effect of M.indicus pranii on the hazard of the composite outcome among HIV and TB pericarditis patients.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.

Validation of a Finite Element Simulation for Predicting Individual Knee Joint Kinematics.

Goal: We introduce an in-vivo validated finite element (FE) simulation approach for predicting individual knee joint kinematics. Our vision is to improve clinicians' understanding of the complex individual anatomy and potential pathologies to improve treatment and restore physiological joint kinematics. Methods: Our 3D FE modeling approach for individual human knee joints is based on segmentation of anatomical structures extracted from routine static magnetic resonance (MR) images. We validate the predictive abilities of our model using static MR images of the knees of eleven healthy volunteers in dedicated knee poses, which are achieved using a customized MR-compatible pneumatic loading device. Results: Our FE simulations reach an average translational accuracy of 2 mm and an average angular accuracy of 1[Formula: see text] compared to the reference knee pose. Conclusions: Reaching high accuracy, our individual FE model can be used in the decision-making process to restore knee joint stability and functionality after various knee injuries.