Bayesian models for multivariate current status data with informative censoring

Multivariate current status data, consist of indicators of whether each of several events occur by the time of a single examination. Our interest focuses on inferences about the joint distribution of the event times. Conventional methods for analysis of multiple event-time data cannot be used because all of the event times are censored and censoring may be informative. Within a given subject, we account for correlated event times through a subject-specific latent variable, conditional upon which the various events are assumed to occur independently. We also assume that each event contributes independently to the hazard of censoring. Nonparametric step functions are used to characterize the baseline distributions of the different event times and of the examination times. Covariate and subject-specific effects are incorporated through generalized linear models. A Markov chain Monte Carlo algorithm is described for estimation of the posterior distributions of the unknowns. The methods are illustrated through application to multiple tumor site data from an animal carcinogenicity study.     

Conditional estimation for generalized linear models when covariates are subject-specific parameters in a mixed model for longitudinal measurements

The relationship between a primary endpoint and features of longitudinal profiles of a continuous response is often of interest, and a relevant framework is that of a generalized linear model with covariates that are subject-specific random effects in a linear mixed model for the longitudinal measurements. Naive implementation by imputing subject-specific effects from individual regression fits yields biased inference, and several methods for reducing this bias have been proposed. These require a parametric (normality) assumption on the random effects, which may be unrealistic. Adapting a strategy of Stefanski and Carroll (1987, Biometrika74, 703-716), we propose estimators for the generalized linear model parameters that require no assumptions on the random effects and yield consistent inference regardless of the true distribution. The methods are illustrated via simulation and by application to a study of bone mineral density in women transitioning to menopause.     

Level of subject-specific detail in musculoskeletal models affects hip moment arm length calculation during gait in pediatric subjects with increased femoral anteversion

Biomechanical parameters of gait such as muscle's moment arm length (MAL) and muscle-tendon length are known to be sensitive to anatomical variability. Nevertheless, most studies rely on rescaled generic models (RGMo) constructed from averaged data of cadaveric measurements in a healthy adult population. As an alternative, deformable generic models (DGMo) have been proposed. These models integrate a higher level of subject-specific detail by applying characteristic deformations to the musculoskeletal geometry. In contrast, musculoskeletal models based on magnetic resonance (MR) images (MRMo) reflect the involved subject's characteristics in every level of the model. This study investigated the effect of the varying levels of subject-specific detail in these three model types on the calculated hip MAL during gait in a pediatric population of seven cerebral palsy subjects presenting aberrant femoral geometry. Our results show large percentage differences in calculated MAL between RGMo and MRMo. Furthermore, the use of DGMo did not uniformly reduce inter-model differences in calculated MAL. The magnitude of these percentage differences stresses the need to take these effects into account when selecting the level of subject-specific detail one wants to integrate in musculoskeletal. Furthermore, the variability of these differences between subjects and between muscles makes it very difficult to a priori estimate their importance for a biomechanical analysis of a certain muscle in a given subject.     

Fitting growth curve models to longitudinal data with missing observations.

We discuss the analysis of growth curve data with missing or incomplete information. The approach is to fit subject-specific models and then to carry out an analysis in terms of the estimated parameters. This achieves reduction of data and eliminates the need for special considerations for subjects with missing data. Although there is no perfect substitute for complete data, our approach provides a way to handle missing data using a straightforward application of well-known statistical methodology.     

A population‐averaged approach to diagnostic test meta‐analysis

The meta‐analysis of diagnostic accuracy studies is often of interest in screening programs for many diseases. The typical summary statistics for studies chosen for a diagnostic accuracy meta‐analysis are often two dimensional: sensitivities and specificities. The common statistical analysis approach for the meta‐analysis of diagnostic studies is based on the bivariate generalized linear‐mixed model (BGLMM), which has study‐specific interpretations. In this article, we present a population‐averaged (PA) model using generalized estimating equations (GEE) for making inference on mean specificity and sensitivity of a diagnostic test in the population represented by the meta‐analytic studies. We also derive the marginalized counterparts of the regression parameters from the BGLMM. We illustrate the proposed PA approach through two dataset examples and compare performance of estimators of the marginal regression parameters from the PA model with those of the marginalized regression parameters from the BGLMM through Monte Carlo simulation studies. Overall, both marginalized BGLMM and GEE with sandwich standard errors maintained nominal 95% confidence interval coverage levels for mean specificity and mean sensitivity in meta‐analysis of 25 of more studies even under misspecification of the covariance structure of the bivariate positive test counts for diseased and nondiseased subjects.     

Feasibility of using MRIs to create subject-specific parallel-mechanism joint models

Musculoskeletal models typically use generic 2D models for the tibiofemoral (TFJ) and patellofemoral (PFJ) joints, with a hinge talocrural joint (TCJ), which are scaled to each subject׳s bone dimensions. Alternatively joints’ measured kinematics in cadavers are well-predicted using 3D cadaver-specific models. These employ mechanisms constrained by the articulations of geometric objects fitted to the joint׳s surfaces.In this study, we developed TFJ, PFJ and TCJ mechanism-based models off MRIs for fourteen participants and compared the estimated kinematics with those from published studies modified to be consistent with mechanisms models and subject-specific anatomical landmarks. The models’ parameters were estimated by fitting spheres to segmented articular cartilage surfaces, while ligament attachment points were selected from their bony attachment regions.Each participant׳s kinematics were estimated by ensuring no length changes in ligaments and constant distances between spheres’ centres. Two parameters’ optimizations were performed; both avoid singularities and one best matches the kinematic patterns off published studies. Sensitivity analysis determined which parameters the models were sensitive to.With both optimization methods, kinematics did not present singularities but correlation values were higher, exceeding 0.6, when matching the published studies. However, ranges of motion (ROM) were different between estimated and published studies. Across participants, models presented large parameter variation. Small variations were found between estimated- and optimized-parameters, and in the estimated-rotations and translations’ means and ROM. Model results were sensitive to changes in distal tibia, talus and patella spheres’ centres. These models can be implemented in subject-specific rigid-body musculoskeletal models to estimate joint moments and loads.     

The interpretation of a regression coefficient.

Zeger, Liang, and Albert (1988, Biometrics 44, 1049-1060) discuss population-averaged and subject-specific models for the analysis of longitudinal data. In their example on respiratory disease in the child and the mother's smoking status, they give an incorrect interpretation to the regression coefficient for the subject-specific model.     

利用混合模型分析地域对国内马尾松生物量的影响

开展全国森林生物量监测和评估,建立适合较大区域范围的通用性立木生物量模型是一项重要的基础工作,而分析森林生物量受不同地域的影响并保证不同尺度范围森林生物量估计值的可靠性,是必须面临的问题。以南方马尾松(Pinus massoniana)地上生物量数据为例,介绍了如何利用混合模型理论来分析地域对马尾松地上生物量的影响以及利用混合模型构建全国通用性立木生物量模型,为得到不同区域尺度范围内可靠的森林生物量评价和估计提供了有效途径。结果表明,混合模型不仅提高了模型的精度和通用性,并且模型中每个参数都有特定的数学含义,通过这些参数很容易分析出随机因子对生物量的影响程度。因此混合模型方法具有较大的灵活性和适应性,可推广到其它通用性模型(如材积方程)的建立。     

Rethinking tropical phenology: insights from long‐term monitoring and novel analytical methods

Here, we introduce the Special Section (SS) on long‐term monitoring and new analytical methods in tropical phenology. The SS puts together nine original papers plus a synthesis, bringing significant advances and new insights into our understanding of tropical phenology across Africa and tropical America. The papers address environmental cues, methodological shortcomings, and provide innovative analytical approaches, opening new pathways, perspective and applications of tropical phenology for forest management and environmental monitoring. The SS is a substantial step toward a more comprehensive overview of trends in tropical phenology, as seven of nine studies evaluate >10‐yr data sets applying new methods of analysis such as hierarchical Bayesian models, generalized additive models, and Fourier analysis. We argue that it is essential to maintain ongoing monitoring programs and build a tropical phenology network at least for long‐term (>10 yr) study sites, providing the means for national and international financial support. Cross‐continental comparisons are now a primary goal, as we work toward a global vision of trends and shifts in tropical phenology in the Anthropocene.     

Computed tomography-based joint locations affect calculation of joint moments during gait when compared to scaling approaches

Hip joint moments are an important parameter in the biomechanical evaluation of orthopaedic surgery. Joint moments are generally calculated using scaled generic musculoskeletal models. However, due to anatomical variability or pathology, such models may differ from the patient's anatomy, calling into question the accuracy of the resulting joint moments. This study aimed to quantify the potential joint moment errors caused by geometrical inaccuracies in scaled models, during gait, for eight test subjects. For comparison, a semi-automatic computed tomography (CT)-based workflow was introduced to create models with subject-specific joint locations and inertial parameters. 3D surface models of the femora and hemipelves were created by segmentation and the hip joint centres and knee axes were located in these models. The scaled models systematically located the hip joint centre (HJC) up to 33.6 mm too inferiorly. As a consequence, significant and substantial peak hip extension and abduction moment differences were recorded, with, respectively, up to 23.1% and 15.8% higher values in the image-based models. These findings reaffirm the importance of accurate HJC estimation, which may be achieved using CT- or radiography-based subject-specific modelling. However, obesity-related gait analysis marker placement errors may have influenced these results and more research is needed to overcome these artefacts.     

Quasi Likelihood/Moment Method for Generalized and Restricted Generalized Poisson Regression Models and Its Application

This paper reviews the generalized Poisson regression model, the restricted generalized Poisson regression model and the mixed Poisson regression (negative binomial regression and Poisson inverse Gaussian regression) models which can be used for regression analysis of counts. The aim of this study is to demonstrate the quasi likelihood/moment method, which is used for estimation of the parameters of mixed Poisson regression models, also applicable to obtain the estimates of the parameters of the generalized Poisson regression and the restricted generalized Poisson regression models. Besides, at the end of this study an application related to this method for zoological data is given.     

Analysis of melanoma onset: assessing familial aggregation by using estimating equations and fitting variance components via Bayesian random effects models.

We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.     

The influence of simulation model complexity on the estimation of internal loading in gymnastics landings

Evaluating landing technique using a computer simulation model of a gymnast and landing mat could be a useful tool when attempting to assess injury risk. The aims of this study were: (1) to investigate whether a subject-specific torque-driven or a subject-specific muscle-driven model of a gymnast is better at matching experimental ground reaction forces and kinematics during gymnastics landings, (2) to calculate their respective simulation run times and (3) to determine what level of model complexity is required to assess injury risk. A subject-specific planar seven-link wobbling mass model of a gymnast and a multi-layer model of a landing mat were developed for this study. Subject-specific strength parameters were determined which defined the maximum voluntary torque/angle/angular velocity relationship about each joint. This relationship was also used to produce subject-specific 'lumped' muscle models for each joint. Kinetic and kinematic data were obtained during landings from backward and forward rotating gymnastics vaults. Both torque-driven and muscle-driven models were capable of producing simulated landings that matched the actual performances (with overall percentage differences between 10.1% and 18.2%). The torque-driven model underestimated the internal loading on joints and bones, resulting in joint reaction forces that were less than 50% of those calculated using the muscle-driven model. Simulation time increased from approximately 3 min (torque driven) to more than 10 min (muscle driven) as model complexity increased. The selection of a simulation model for assessing injury risk must consider the need for determining realistic internal forces as the priority despite increases in simulation run time.     

Functional mixed effects models

In this article, a new class of functional models in which smoothing splines are used to model fixed effects as well as random effects is introduced. The linear mixed effects models are extended to nonparametric mixed effects models by introducing functional random effects, which are modeled as realizations of zero-mean stochastic processes. The fixed functional effects and the random functional effects are modeled in the same functional space, which guarantee the population-average and subject-specific curves have the same smoothness property. These models inherit the flexibility of the linear mixed effects models in handling complex designs and correlation structures, can include continuous covariates as well as dummy factors in both the fixed or random design matrices, and include the nested curves models as special cases. Two estimation procedures are proposed. The first estimation procedure exploits the connection between linear mixed effects models and smoothing splines and can be fitted using existing software. The second procedure is a sequential estimation procedure using Kalman filtering. This algorithm avoids inversion of large dimensional matrices and therefore can be applied to large data sets. A generalized maximum likelihood (GML) ratio test is proposed for inference and model selection. An application to comparison of cortisol profiles is used as an illustration.     

The non-linear response of a muscle in transverse compression: assessment of geometry influence using a finite element model

Most recent finite element models that represent muscles are generic or subject-specific models that use complex, constitutive laws. Identification of the parameters of such complex, constitutive laws could be an important limit for subject-specific approaches. The aim of this study was to assess the possibility of modelling muscle behaviour in compression with a parametric model and a simple, constitutive law. A quasi-static compression test was performed on the muscles of dogs. A parametric finite element model was designed using a linear, elastic, constitutive law. A multi-variate analysis was performed to assess the effects of geometry on muscle response. An inverse method was used to define Young's modulus. The non-linear response of the muscles was obtained using a subject-specific geometry and a linear elastic law. Thus, a simple muscle model can be used to have a bio-faithful, biomechanical response.     

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.     

Modeling pilus structures from sparse data

Bacterial Type II secretion systems (T2SS) and type IV pili (T4P) biogenesis machineries share the ability to assemble thin filaments from pilin protein subunits in the plasma membrane. Here we describe in detail the calculation strategy that served to determine a detailed atomic model of the T2SS pilus from Klebsiella oxytoca (Campos et al., PNAS 2010). The strategy is based on molecular modeling with generalized distance restraints and experimental validation (salt bridge charge inversion; double cysteine substitution and crosslinking). It does not require directly fitting structures into an envelope obtained from electron microscopy, but relies on lower resolution information, in particular the symmetry parameters of the helix forming the pilus. We validate the strategy with T4P where either a higher resolution structure is available (for the gonococcal (GC) pilus from Neisseria gonorrhoeae), or where we can compare our results to additional experimental data (for Vibrio cholerae TCP). The models are of sufficient precision to compare the architecture of the different pili in detail.     

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

     

In the Absence of Effector Proteins,the Pseudomonas aeruginosa Type Three Secretion System Needle Tip Complex Contributes to Lung Injury and Systemic Inflammatory Responses

Herein we describe a pathogenic role for the Pseudomonas aeruginosa type three secretion system (T3SS) needle tip complex protein, PcrV, in causing lung endothelial injury. We first established a model in which P. aeruginosa wild type strain PA103 caused pneumonia-induced sepsis and distal organ dysfunction. Interestingly, a PA103 derivative strain lacking its two known secreted effectors, ExoU and ExoT [denoted PA103 (ΔU/ΔT)], also caused sepsis and modest distal organ injury whereas an isogenic PA103 strain lacking the T3SS needle tip complex assembly protein [denoted PA103 (ΔPcrV)] did not. PA103 (ΔU/ΔT) infection caused neutrophil influx into the lung parenchyma, lung endothelial injury, and distal organ injury (reminiscent of sepsis). In contrast, PA103 (ΔPcrV) infection caused nominal neutrophil infiltration and lung endothelial injury, but no distal organ injury. We further examined pathogenic mechanisms of the T3SS needle tip complex using cultured rat pulmonary microvascular endothelial cells (PMVECs) and revealed a two-phase, temporal nature of infection. At 5-hours post-inoculation (early phase infection), PA103 (ΔU/ΔT) elicited PMVEC barrier disruption via perturbation of the actin cytoskeleton and did so in a cell death-independent manner. Conversely, PA103 (ΔPcrV) infection did not elicit early phase PMVEC barrier disruption. At 24-hours post-inoculation (late phase infection), PA103 (ΔU/ΔT) induced PMVEC damage and death that displayed an apoptotic component. Although PA103 (ΔPcrV) infection induced late phase PMVEC damage and death, it did so to an attenuated extent. The PA103 (ΔU/ΔT) and PA103 (ΔPcrV) mutants grew at similar rates and were able to adhere equally to PMVECs post-inoculation indicating that the observed differences in damage and barrier disruption are likely attributable to T3SS needle tip complex-mediated pathogenic differences post host cell attachment. Together, these infection data suggest that the T3SS needle tip complex and/or another undefined secreted effector(s) are important determinants of P. aeruginosa pneumonia-induced lung endothelial barrier disruption.     

The Pseudomonas aeruginosa reference strain PA14 displays increased virulence due to a mutation in ladS

Pseudomonas aeruginosa is a pathogen that causes acute and chronic infections in a variety of hosts. The pathogenic potential of P. aeruginosa is strain-dependent. PA14 is a highly virulent strain that causes disease in a wide range of organisms, whereas PAO1 is moderately virulent. Although PA14 carries pathogenicity islands that are absent in PAO1, the presence or absence of specific gene clusters is not predictive of virulence. Here, we show that the virulent strain PA14 has an acquired mutation in the ladS gene. This mutation has a deleterious impact on biofilm, while it results in elevated type III secretion system (T3SS) activity and increased cytotoxicity towards mammalian cells. These phenotypes can be reverted by repairing the ladS mutation on the PA14 genome. The RetS/LadS/GacS signaling cascade is associated with virulence and the switch between acute and chronic infections. RetS is a sensor that down-regulates biofilm formation and up-regulates the T3SS. Mutations in retS are acquired in strains isolated from chronically infected cystic fibrosis patients and lead to hyperbiofilm formation and reduced cytotoxicity. Conversely, the LadS sensor promotes biofilm formation and represses the T3SS. We conclude that the ladS mutation is partly responsible for the high cytotoxicity of PA14, and our findings corroborate the central role of RetS and LadS in the switch between acute and chronic infections. Given the extensive use of the reference strain PA14 in infection and virulence models, the bias caused by the ladS mutation on the observed phenotypes will be crucial to consider in future research.