Analysis Of Longitudinal Data Research Articles

Longitudinal Analysis of CD4 Cell Counts Data on HIV Patients Initiated on Anti-retroviral Therapy: Case of Ayder Comprehensive Specialized Hospital, Tigray, Ethiopia

Longitudinal Analysis of CD4 Cell Counts Data on HIV Patients Initiated on Anti-retroviral Therapy: Case of Ayder Comprehensive Specialized Hospital, Tigray, Ethiopia

Longitudinal patterns of initiation, persistence, and cycling on preexposure prophylaxis among female sex workers and adolescent girls and young women in South Africa.

Female sex workers (FSW) and adolescent girls and young women (AGYW) face a disproportionately high risk of HIV in South Africa. Oral preexposure prophylaxis (PrEP) can avert new infections, but its effectiveness is linked to consistent use. Early discontinuation of PrEP in this population is high, but less is known about longitudinal patterns of PrEP use, including patterns of re-initiation and cycling. Longitudinal descriptive analysis of routine program data. Between 2016 and 2021, 40 681 FSW and AGYW initiated PrEP at TB HIV Care, the largest PrEP provider to this population in South Africa and were included. Using survival analyses and group-based trajectory modeling, we described patterns of initiation, discontinuation, re-initiation, and cycling. Total initiations increased over the life of the program for both FSW and AGYW. About 40% of FSW [0.41, 95% confidence interval (CI) [0.40-0.42]] and AGYW (0.38, 95% CI [0.37-0.38]) remained on PrEP at one month. FSW were more likely to restart PrEP, however <10% restarted PrEP within a year of initiation. Three latent trajectory groups of PrEP use were identified for FSW (low use, early cycling, and ongoing cycling) and two for AGYW (low use and ongoing cycling). Persistence was negatively associated with initiation among AGYW, but there was no clear relationship among FSW. Those initiating later in the program and older women had a reduced risk of discontinuation. Persistence on PrEP was low, but cycling on and off PrEP was common, with early missed visits and inconsistent, but ongoing use. A push to increase PrEP initiations needs to factor in readiness and persistence support, to achieve public health impact.

Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect.

Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated with high power, but sometimes at the cost of inflated type I error. Approaches to overcome this problem were published recently, such as model-averaging across drug models (MAD), individual model-averaging (IMA), and combined Likelihood Ratio Test (cLRT). This work aimed to assess seven NLMEM approaches in the same framework: treatment effect assessment in balanced two-armed designs using real natural history data with or without the addition of simulated treatment effect. The approaches are MAD, IMA, cLRT, standard model selection (STDs), structural similarity selection (SSs), randomized cLRT (rcLRT), and model-averaging across placebo and drug models (MAPD). The assessment included type I error, using Alzheimer's Disease Assessment Scale-cognitive (ADAS-cog) scores from 817 untreated patients and power and accuracy in the treatment effect estimates after the addition of simulated treatment effects. The model selection and averaging among a set of pre-selected candidate models were driven by the Akaike information criteria (AIC). The type I error rate was controlled only for IMA and rcLRT; the inflation observed otherwise was explained by the placebo model misspecification and selection bias. Both IMA and rcLRT had reasonable power and accuracy except under a low typical treatment effect.

Periodontal health and patient-reported outcomes: A longitudinal analysis of data from non-specialist practice settings.

To explore the associations between periodontal health and patient-reported outcomes (PROs), accounting for changes over time, in a large, non-specialist dental practice patient cohort. This longitudinal study used data from 13,162 dentate patients, collected by 162 dentists at routine appointments between May 2013 and April 2020, in 238 non-specialist dental practices across the United Kingdom. Dentists collected data, as part of routine clinical care, on periodontal probing pocket depths, alveolar bone loss, bleeding on probing, as well as a range of covariates. Patients inputted data on outcomes (oral pain/discomfort, dietary restrictions, and dental appearance). Mixed-effects logistic regression analysis was used to investigate the associations between periodontal health and PROs. Models accounted for clustering at the patient and dentist level and were adjusted for time and variables which were thought to confound these associations. The odds of all PROs tended to increase with worsening periodontal parameters. For example, the odds of reporting pain in the worst periodontal health category were 1.99 (95% confidence interval: 1.57-2.53) times higher than in the best periodontal health category. This study confirms, using a large longitudinal dataset from a unique non-specialist setting, the associations between poorer periodontal health and poorer PROs.

Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study

BackgroundMultivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability.MethodsWe investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models.ResultsIn a cohort of 3539 participants, longitudinal information from 35 variables that were repeatedly collected in 6 exam visits over 15 years improved subsequent long-term (17 years after) risk prediction by up to 8.3% in C-index compared to using baseline data (0.78 vs. 0.72), and up to approximately 4% compared to using the last observed CS data (0.75). Time-varying AUC was also higher in models using longitudinal data (0.86–0.87 at 5 years, 0.79–0.81 at 10 years) than using baseline or last observed CS data (0.80–0.86 at 5 years, 0.73–0.77 at 10 years). Comparative model interpretability analysis revealed the impact of longitudinal variables on model prediction on both the individual and global scales among different modeling strategies, as well as identifying the best time windows and best timing within that window for event prediction. The best strategy to incorporate longitudinal data for accuracy was time series massive feature extraction, and the easiest interpretable strategy was trajectory clustering.ConclusionOur analysis demonstrates the added value of longitudinal data in predictive accuracy and epidemiological utility in cardiovascular risk survival analysis in young adults via a unified, scalable framework that compares model performance and explainability. The framework can be extended to a larger number of variables and other longitudinal modeling methods.Trial registrationClinicalTrials.gov Identifier: NCT00005130, Registration Date: 26/05/2000.

Implementation and Utilization of a Web-Based Departmental Annual Faculty Review Program.

Supporting faculty in their professional development is a central mission of academic medical centers (AMCs), assisted by routine assessment of faculty development. However, AMCs face barriers to standard faculty assessment, particularly among large departments with diverse faculty roles. We report on the development, implementation, and utilization of a standardized, web-based faculty annual career review (FACR) process deployed within the Department of Medicine at a large AMC, composed of 22 divisions and 1400 faculty. The FACR process was developed to achieve the following goals: Creation of a trackable review of professional goals and barriers to professional development; provide longitudinal faculty reflection on professional growth; and provide data to identify faculty development needs to target initiatives. The FACR was initiated in 2008. We provide specific case examples of utilization of the FACR data for targeted faculty development initiatives, including (1) division benchmarking; (2) targeting career opportunities to individual faculty members; and (3) longitudinal data analysis of 5-year trends of FACR response, including subanalysis by gender, to target faculty development initiatives across the DOM. In summary, we demonstrate successful development, implementation, and utilization of a web-based standard faculty annual review program within a large AMC that allows for systematic evaluation and reflection of professional development by faculty members and their supervisors and allows for data collection and analysis to help target faculty development initiatives.

Glycemia is associated with subclinical atherosclerosis through renal function in nondiabetic apparently healthy adults: a mediation analysis.

The causative associations between glycemia and early alterations in renal and vascular function remain unclear. To examine the interplay among glycemia, renal function, and markers of subclinical atherosclerosis in apparently healthy subjects. Nondiabetic (30-60 years old) individuals (n = 205) without chronic kidney disease or cardiovascular disease were consecutively recruited from a cardiovascular prevention clinic. All subjects underwent arterial stiffness assessment by measuring the carotid-femoral pulse wave velocity (cfPWV). Glomerular filtration rate (GFR) was estimated by CKD-EPI equation. Study procedures were identical in the two visits (median follow-up 66 months). We employed structural equation modeling (SEM) analysis to investigate the directionality of associations. Baseline fasting plasma glucose (FPG) was independently and inversely associated with GFR (p = 0.008). GFR was significantly associated with cfPWV (p < 0.001) at baseline. By SEM analysis decreasing baseline GFR directly correlated with increasing cfPWV (p = 0.003) whereas FPG correlated with cfPWV indirectly through GFR (mediation) (P = 0.032). FPG did not mediate the effect of GFR on cfPWV (P = 0.768). SEM analysis of longitudinal data revealed bidirectional correlations between changes in FPG and GFR (P < 0.001). Alterations in GFR were directly related to changes in cfPWV (p < 0.001) whereas FPG only indirectly correlated with cfPWV through GFR changes (P = 0.002). In apparently healthy nondiabetic subjects, the association between baseline or longitudinal glycemia levels and arterial stiffening was indirect, consistently mediated by renal function status. These findings provide the first clinical evidence supporting the directionality between kidney function and glycemia in nondiabetic subjects leading to vascular dysfunction. In apparently healthy nondiabetic subjects, without cardiovascular disease or chronic kidney disease, the association between baseline or longitudinal glycemia levels and arterial stiffening was indirect, consistently mediated by renal function status.

Temporal trends in, and associations of, early-career general practitioner prescriptions of second-line Type 2 Diabetes medications, 2010-2018.

Second-line pharmacotherapy for Type 2 Diabetes Mellitus ('diabetes') is necessary for optimal glycaemic control and preventing longer-term complications. We aimed to describe temporal trends in, and associations of, Australian general practitioner (GP) registrars' prescription, and initiation, of 'new' second-line oral agents (dipeptidyl peptidase 4 inhibitors, sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 agonists) compared to sulphonylureas. A longitudinal analysis (2010-2018) of data from the Registrar Clinical Encounters in Training project. Analysis included any diabetes problem/diagnosis that involved prescription of sulphonylureas or 'new' oral agents. Simple and multiple logistic regression models were fitted within the generalised estimating equations framework. 2333 registrars recorded 6064 diabetes problems/diagnoses (1.4%). 835 problems/diagnoses involved sulphonylurea or 'new' medication prescription. Of these, 61.0% [95% CI:57.4-64.4] involved 'new' medication prescription. 230 problems/diagnoses involved sulphonylurea or 'new' medication initiation, with 77% [95%CI:70.8-82.1] involving a 'new' medication. There was a significant 52% per year increase in prescribing (OR = 1.52[95% CI:1.38-1.68],p<0.001), and a 77% per (two-to-three-year) time-interval increase in initiation (OR = 1.77,[95% CI:1.30-2.43],p = <0.001) of 'new' medications compared to sulphonylureas. 'New' medications were prescribed less for non-English-speaking patients. There was some regional variation in prescribing. Registrar uptake of 'new' oral agents compared to sulphonylureas has increased rapidly.

Trends and consequences of lipoprotein(a) testing: Cross-sectional and longitudinal health insurance claims database analyses

Background and aimsLipoprotein(a) (Lp(a)) is associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD). Our goal was to characterize patients undergoing Lp(a) testing and to assess the impact of Lp(a) testing on treatment changes and subsequent ASCVD events. MethodsA cross-sectional and a longitudinal claims data analysis were performed on 4 million patient records in Germany. Patients were followed up for a maximum of 4 years. ResultsIn 2015 and 2018, 0.25% and 0.34% of patients were tested, respectively. Testing was more frequent in younger women in the overall population, and in men in the ASCVD population. Patients tested for Lp(a) had more comorbidities and higher ASCVD risk compared to matched control patients. ASCVD hospitalizations were more frequent prior to the first Lp(a) test (5.55 vs 1.42 per 100/person-years). The mortality rate of the Lp(a)-tested cohort and the control group was similar. Mortality was lower in patients with prior ASCVD and Lp(a) testing compared to matched controls with prior ASCVD and no Lp(a) test (2.30 vs 3.64 per 100/person-years, p <0.001). Patients with Lp(a) test received more laboratory examinations and cardiovascular medications and had more visits with specialized physicians. ConclusionsLp(a) testing is rarely performed even in patients with very high cardiovascular risk. Patients tested for Lp(a) have more comorbidities and a higher ASCVD risk. Lp(a) testing is associated with more intensive preventive treatment and with positive effects on clinical outcomes and survival. The data support the value of Lp(a) measurements to characterize ASCVD risk and to improve ASCVD prevention.

Multicollinearity and Linear Predictor Link Function Problems in Regression Modelling of Longitudinal Data

In the longitudinal data analysis we integrate flexible linear predictor link function and high-correlated predictor variables. Our approach uses B-splines for non-parametric part in the linear predictor component. A generalized estimation equation is used to estimate the parameters of the proposed model. We assess the performance of our proposed model using simulations and an application to an analysis of acquired immunodeficiency syndrome data set.

Natural cubic splines for the analysis of Alzheimer's clinical trials.

Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.

Predictive factors associated with trajectory of job satisfaction of workers with physical disabilities: A latent growth curve analysis.

Job satisfaction is a highly useful factor in maintaining a successful work life for the disabled. This study aimed to evaluate how job satisfaction among workers with physical disabilities evolves over time, and to identify predictive factors that influence the trajectory of job satisfaction. A longitudinal data analysis using the three-year databases of the 2016-2018 Employment Panel Survey for the Disabled (n = 693). A latent growth curve model analysis was conducted to determine the trajectory of the job satisfaction among workers with physical disabilities, as well as the predictive factors affecting it. Job satisfaction among workers with physical disabilities increased over time, and there were substantial individual differences in the trajectory of the job satisfaction. The significant predictors for the job satisfaction over the three years were degree of disability, computer usage ability, employment support services, and experience of discrimination. Gender, educational background, interpersonal relationship/organizational adaptability, convenience facilities for the disabled, job suitability, disability acceptability, and self-esteem had a considerable impact on the intercept of the job satisfaction, but not on the slope. The findings would inform healthcare providers to establish mid-to-long-term vocational rehabilitation intervention plans that could increase the job satisfaction among workers with physical disabilities.

Accelerometer-Measured Physical Activity and Sedentary Time among Children in Japan before and during COVID-19: A Cross-Sectional and Longitudinal Analysis.

This study examined changes in physical activity (PA), sedentary behavior (SB), screen time, sleep, and executive function among Japanese preschoolers between COVID-19 pre-pandemic and pandemic periods, using cross-sectional and longitudinal data. Accelerometer data from 63 children aged 5-6 years were collected from three kindergartens in Tokyo, Japan, in late 2019 (pre-COVID-19). This was compared to the data of 49 children aged 5-6 years from the same kindergartens, collected in late 2020 (during COVID-19). Sixteen children in the pre-COVID-19 cohort also participated in the 2020 survey and provided data for the longitudinal analysis. The mean minutes of PA, SB, screen time, and sleep duration, as well as executive function, were compared between the pre- and during COVID-19 cohorts. After adjusting for school, sex, and accelerometer wear time, there were no significant differences in any of the measured outcomes between the two cohorts. However, the analysis of longitudinal data revealed significant increases in time spent in SB and on screens, and a decrease in light-intensity PA and sleep duration during the pandemic compared to the pre-pandemic period. Results suggest that, despite the COVID-19 pandemic, young children's activity levels and SB did not significantly differ from pre-pandemic levels. However, school-aged children's SB, light PA, and sleep time were affected, although this cannot be disentangled from the effects of the transition to school.

Presentation of ventriculomegaly at 11-14weeks of gestation: An analysis of longitudinal data.

The aim of this study was to examine the value of the sonographic measurements of the choroid plexus and the lateral ventricles at 11-14 gestational weeks in fetuses that had the diagnosis of second-trimester ventriculomegaly (VM) as a clinical reference. The standard axial plane used for biparietal diameter measurement from 2D stored images in the first trimester was used to calculate the ratio between the choroid plexus and lateral ventricle diameter (PDVDR), the choroid plexus and lateral ventricle length (PLVLR) and the choroid plexus and lateral ventricle area (PAVAR) in 100 normal and 15 fetuses diagnosed with second-trimester VM. In fetuses with VM, the measurements of PDVDR, PLVLR and PAVAR were all significantly smaller compared to normal fetuses (p=<0.001, <0.001, <0.01). Four out of seven cases with mild VM had measurements below the 5th percentile (57%). 75% of cases with moderate or severe VM had at least one measurement below the 5th percentile. Since the axial plane of the fetal head is obtained in all first-trimester routine screenings, the measurements of PDVDR, PLVLR and PAVAR could easily be integrated into routine examinations for an early detection of VM.

Direct Versus Indirect Query Performance of ICD-9/-10 Coding to Identify Anaphylaxis

Anaphylaxis is an often under=diagnosed, severe allergic event for which epidemiological data are sporadic. Researchers have leveraged administrative and claims data algorithms to study large databases of anaphylactic events; however, little longitudinal data analysis is available after transition to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). Study longitudinal trends in anaphylaxis incidence using direct and indirect query methods. Emergency department (ED) and inpatient data were analyzed from a large state health care administration database from 2011 to 2020. Incidence was calculated using direct queries of anaphylaxis ICD-9-CM and ICD-10-CM codes and indirect queries using a symptom-based ICD-9-CM algorithm and forward mapped ICD-10-CM version to identify undiagnosed anaphylaxis episodes and to assess algorithm performance at the population level. An average of 2.4 million inpatient and 7.5 million ED observations/y were analyzed. Using the direct query method, annual ED anaphylaxis cases increased steadily from 1,454 (2011) to 4,029 (2019) then declined to 3,341 in 2020 during the coronavirus disease 2019 (COVID-19) pandemic. In contrast, inpatient cases remained relatively steady, with a slight decline after 2015 during the ICD version transition, until a significant drop occurred in 2020. Using the indirect queries, anaphylaxis cases increased markedly after the ICD transition year, especially involving drug-related anaphylaxis. Nontypical drug associations with anaphylaxis episodes using the ICD-10-CM version of the algorithm suggest poor performance with drug-related codes. Further, the increased granularity of ICD-10-CM identified potential limitations of a previously validated symptom-based ICD-9-CM algorithm used to detect undiagnosed cases.

Linking negative affect, personality and social conditions to structural brain development during the transition from late adolescent to young adulthood

BackgroundThe transition from late adolescence to early adulthood is a period that experiences a surge of life changes and brain reorganization caused by internal and external factors, including negative affect, personality, and social conditions. MethodsNon-imaging phenotype and structural brain variables were available on 497 healthy participants (279 females and 218 males) between 17 and 22 years old. We used sparse canonical correlation analysis (sCCA) on the high-dimensional and longitudinal data to extract modes with maximum covariation between structural brain changes and negative affect, personality, and social conditions. ResultsSeparate sCCAs for cortical volume, cortical thickness, cortical surface area and subcortical volume confirmed that each imaging phenotype was correlated with non-imaging features (sCCA |r| range: 0.21–0.38, all pFDR < 0.01). Bilateral superior frontal, left caudal anterior cingulate and bilateral caudate had the highest canonical cross-loadings (|ρ| = 0.15–0.32). In longitudinal data analysis, scan-interval, negative affect, and enthusiasm had the highest association with structural brain changes (|ρ| = 0.07–0.38); at baseline, intellect and politeness were associated with individual variability in the structural brain (|ρ| = 0.10–0.25). LimitationsThe present study used non-imaging variables only at baseline, making it impossible to explore the relationship between changing behavior and structural brain development. ConclusionsIndividual structural brain changes are associated with multiple factors. In addition to time-dependent variables, we find that negative affect, enthusiasm and social support play a numerically weak but significant role in structural brain development during the transition from late adolescence to young adulthood.

Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI

Objective. In the field of radiation oncology, the benefit of MRI goes beyond that of providing high soft-tissue contrast images for staging and treatment planning. With the recent clinical introduction of hybrid MRI linear accelerators it has become feasible to map physiological parameters describing diffusion, perfusion, and relaxation during the entire course of radiotherapy, for example. However, advanced data analysis tools are required for extracting qualified prognostic and predictive imaging biomarkers from longitudinal MRI data. In this study, we propose a new prediction framework tailored to exploit temporal dynamics of tissue features from repeated measurements. We demonstrate the framework using a newly developed decomposition method for tumor characterization. Approach. Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing: T 2-weighted multi-echo images obtained for two mouse models of pancreatic cancer, and diffusion-weighted images for patients with brain metastases. Initially, the data was decomposed using the novel monotonous slope non-negative matrix factorization (msNMF) tailored for MR data. The following processing consisted of a tumor heterogeneity assessment using descriptive statistical measures, robust linear modelling to capture temporal changes of these, and finally logistic regression analysis for stratification of tumors and volumetric outcome. Main Results. The framework was able to classify the two pancreatic tumor types with an area under curve (AUC) of 0.999, P < 0.001 and predict the tumor volume change with a correlation coefficient of 0.513, P = 0.034. A classification of the human brain metastases into responders and non-responders resulted in an AUC of 0.74, P = 0.065. Significance. A general data processing framework for analyses of longitudinal MRI data has been developed and applications were demonstrated by classification of tumor type and prediction of radiotherapy response. Further, as part of the assessment, the merits of msNMF for tumor tissue decomposition were demonstrated.

Progressive changes in residual gradient after aortic coarctation repair and its role in the prediction of reintervention: A longitudinal data analysis.

Repair of aortic coarctation through left thoracotomy is the standard treatment when anatomically feasible. Long-term outcomes are well studied, including the need for reintervention. However, the timely variation in residual gradients across the repaired segment is ill-defined. The aim of this work was to study the progressive changes of estimated peak gradient (ePG) acquired by transthoracic continuous-wave Doppler echocardiography across the aortic arch after repair and to assess the role of timing of assessment and values of ePG in prediction of reintervention. All eligible patients for this study who underwent aortic coarctation repair through left thoracotomy from 2001 to 2017 were reviewed. Details of the aortic arch dimensions and associated lesions were obtained by transthoracic echocardiography (TTE). The primary outcome was the ePG across the aortic arch after repair. Longitudinal data analyses with mixed effect modeling were used to determine independent predictors for ePGs. A total of 312 patients were included. Median age and weight were 30 days and 4 kg, respectively. Associated lesions included ventricular septal defect (VSD) (53%), bicuspid aortic valve (53%) and mitral stenosis (25%). Over 15-years follow-up the freedom from reintervention was 92.3%, while 24 out of the 312 patients underwent reintervention (7.7%). Longitudinal data analyses of serial 2566 TTE studies were done. The graphical display showed that the ePG across coarctation area in the first postoperative TTE was the most notable difference between those who underwent reintervention and those who did not. Further testing with proportional hazard and logistic regression modeling confirmed this finding. The area under receiver operating curve statistics showed that an ePG of 25 mmHg is an optimal cutoff value for the prediction of the reintervention. The ePG acquired in the first postoperative TTE is the most important predictor for reinterventions. The presence of VSD is associated with decreased ePGs. We propose that an ePG in the first postoperative TTE of 25 mmHg or more is a strong predictor for the need of reintervention.

Hierarchical Geodesic Polynomial Model for Multilevel Analysis of Longitudinal Shape.

Longitudinal analysis is a core aspect of many medical applications for understanding the relationship between an anatomical subject's function and its trajectory of shape change over time. Whereas mixed-effects (or hierarchical) modeling is the statistical method of choice for analysis of longitudinal data, we here propose its extension as hierarchical geodesic polynomial model (HGPM) for multilevel analyses of longitudinal shape data. 3D shapes are transformed to a non-Euclidean shape space for regression analysis using geodesics on a high dimensional Riemannian manifold. At the subject-wise level, each individual trajectory of shape change is represented by a univariate geodesic polynomial model on timestamps. At the population level, multivariate polynomial expansion is applied to uni/multivariate geodesic polynomial models for both anchor points and tangent vectors. As such, the trajectory of an individual subject's shape changes over time can be modeled accurately with a reduced number of parameters, and population-level effects from multiple covariates on trajectories can be well captured. The implemented HGPM is validated on synthetic examples of points on a unit 3D sphere. Further tests on clinical 4D right ventricular data show that HGPM is capable of capturing observable effects on shapes attributed to changes in covariates, which are consistent with qualitative clinical evaluations. HGPM demonstrates its effectiveness in modeling shape changes at both subject-wise and population levels, which is promising for future studies of the relationship between shape changes over time and the level of dysfunction severity on anatomical objects associated with disease.

Robust Bayesian growth curve modeling: A tutorial using JAGS

Latent growth curve models (LGCM) are widely used in longitudinal data analysis, and robust methods can be used to model error distributions for non-normal data. This tutorial introduces how to modellinear, non-linear, and quadratic growth curve models under the Bayesian framework and uses examples to illustrate how to model errors using t, exponential power, and skew-normal distributions. The code of JAGS models is provided and implemented by the R package runjags. Model diagnostics and comparisons are briefly discussed.