90 research outputs found
Joint Modelling of Longitudinal and Survival Data with Applications in Heart Valve Data
__Abstract__
The heart is one of the most important organs in the entire human body. Specifically, it is
a pump composed of muscle which pumps blood throughout the blood vessels to various parts
of the body by repeated rhythmic contractions. The four heart valves determine the pathway
of blood flow through the heart and they normally allow blood flow in only one direction
through the heart. Moreover, they open or close incumbent upon differential blood pressure
on each side. Specifically, the four valves are: the tricuspid valve, the pulmonary valve, the
mitral valve and the aortic valve. Figure 1.1, represents graphically the heart anatomy. The
blood flows from the right atrium to the right ventricle through the tricuspid valve. Thereafter,
the blood flows through the pulmonary valve to the lungs, where oxygenation takes place.
Next, the blood re-enters the heart into the left atrium, through the mitral valve into the left
ventricle. Finally, it enters the aorta through the aortic valve. Another important part of the
heart is the aortic root which connects the heart to the systemic circulation.
Heart valve disease occurs when one or more valves are not functioning properly due
to stenosis and/or regurgitation. Valve stenosis is the disease in which the opening of the
valve is narrowed, while valve regurgitation or insufficiency is the leaking of the valve that
causes blood to flow in the reverse direction during ventricular diastole. Echoca
Improved Dynamic Predictions from Joint Models of Longitudinal and Survival Data with Time-Varying Effects using P-splines
In the field of cardio-thoracic surgery, valve function is monitored over
time after surgery. The motivation for our research comes from a study which
includes patients who received a human tissue valve in the aortic position.
These patients are followed prospectively over time by standardized
echocardiographic assessment of valve function. Loss of follow-up could be
caused by valve intervention or the death of the patient. One of the main
characteristics of the human valve is that its durability is limited.
Therefore, it is of interest to obtain a prognostic model in order for the
physicians to scan trends in valve function over time and plan their next
intervention, accounting for the characteristics of the data.
Several authors have focused on deriving predictions under the standard joint
modeling of longitudinal and survival data framework that assumes a constant
effect for the coefficient that links the longitudinal and survival outcomes.
However, in our case this may be a restrictive assumption. Since the valve
degenerates, the association between the biomarker with survival may change
over time.
To improve dynamic predictions we propose a Bayesian joint model that allows
a time-varying coefficient to link the longitudinal and the survival processes,
using P-splines. We evaluate the performance of the model in terms of
discrimination and calibration, while accounting for censoring
Integrating Latent Classes in the Bayesian Shared Parameter Joint Model of Longitudinal and Survival Outcomes
Cystic fibrosis is a chronic lung disease which requires frequent patient
monitoring to maintain lung function over time and minimize onset of acute
respiratory events known as pulmonary exacerbations. From the clinical point of
view it is important to characterize the association between key biomarkers
such as and time-to first exacerbation. Progression of the disease is
heterogeneous, yielding different sub-groups in the population exhibiting
distinct longitudinal profiles. It is desirable to categorize these unobserved
sub-groups (latent classes) according to their distinctive trajectories.
Accounting for these latent classes, in other words heterogeneity, will lead to
improved estimates of association arising from the joint longitudinal-survival
model.
The joint model of longitudinal and survival data constitutes a popular
framework to analyze such data arising from heterogeneous cohorts. In
particular, two paradigms within this framework are the shared parameter joint
models and the joint latent class models. The former paradigm allows one to
quantify the strength of the association between the longitudinal and survival
outcomes but does not allow for latent sub-populations. The latter paradigm
explicitly postulates the existence of sub-populations but does not directly
quantify the strength of the association.
We propose to integrate latent classes in the shared parameter joint model in
a fully Bayesian approach, which allows us to investigate the association
between and time-to first exacerbation within each latent class. We,
furthermore, focus on the selection of the optimal number of latent classes
Dynamic Predictions with Time-Dependent Covariates in Survival Analysis using Joint Modeling and Landmarking
A key question in clinical practice is accurate prediction of patient
prognosis. To this end, nowadays, physicians have at their disposal a variety
of tests and biomarkers to aid them in optimizing medical care. These tests are
often performed on a regular basis in order to closely follow the progression
of the disease. In this setting it is of medical interest to optimally utilize
the recorded information and provide medically-relevant summary measures, such
as survival probabilities, that will aid in decision making. In this work we
present and compare two statistical techniques that provide dynamically-updated
estimates of survival probabilities, namely landmark analysis and joint models
for longitudinal and time-to-event data. Special attention is given to the
functional form linking the longitudinal and event time processes, and to
measures of discrimination and calibration in the context of dynamic
prediction.Comment: 34 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:1303.279
A Bayesian joint model for zero‐inflated integers and left‐truncated event times with a time‐varying association: Applications to senior health care
Population aging in most industrialized societies has led to a dramatic increase in emergency medical demand among the elderly. In the context of private health care, an optimal allocation of the medical resources for seniors is commonly done by forecasting their life spans. Accounting for each subject's particularities is therefore indispensable, so the available data must be processed at an individual level. We use a large and unique dataset of insured parties aged 65 and older to appropriately relate the emergency care usage with mortality risk. Longitudinal and time‐to‐event processes are jointly modeled, and their underlying relationship can therefore be assessed. Such an application, however, requires some special features to also be considered. First, longitudinal demand for emergency services exhibits a nonnegative integer response with an excess of zeros due to the very nature of the data. These subject‐specific responses are handled by a zero‐inflated version of the hierarchical negative binomial model. Second, event times must account for the left truncation derived from the fact that policyholders must reach the age of 65 before they may begin to be observed. Consequently, a delayed entry bias arises for those individuals entering the study after this age threshold. Third, and as the main challenge of our analysis, the association parameter between both processes is expected to be age‐dependent, with an unspecified association structure. This is well‐approximated through a flexible functional specification provided by penalized B‐splines. The parameter estimation of the joint model is derived under a Bayesian scheme
Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset
BACKGROUND: Attenuated decreases in lung function can signal the onset of acute respiratory events known as pulmonary exacerbations (PEs) in children and adolescents with cystic fibrosis (CF). Univariate joint modeling facilitates dynamic risk prediction of PE onset and accounts for measurement error of the lung function marker. However, CF is a multi-system disease and the extent to which simultaneously modeling growth and nutrition markers improves PE predictive accuracy is unknown. Furthermore, it is unclear which routinely collected clinical indicators of growth and nutrition in early life predict PE onset in CF. METHODS: Using a longitudinal cohort of 17,100 patients aged 6-20 years (US Cystic Fibrosis Foundation Patient Registry; 2003-2015), we fit a univariate joint model of lung-function decline and PE onset and contrasted its predictive performance with a class of multivariate joint models that included combinations of growth markers as additional submodels. Outcomes were longitudinal lung function (forced expiratory volume in 1 s of % predicted), percentiles of body mass index, weight-for-age and height-for-age and PE onset. Relevant demographic/clinical covariates were included in submodels. We implemented a univariate joint model of lung function and time-to-PE and four multivariate joint models including growth outcomes. RESULTS: All five joint models showed that declining lung function corresponded to slightly increased risk of PE onset (hazard ratio from univariate joint model: 0.97, P 0.70). None of the growth markers alongside lung function as outcomes in multivariate joint modeling appeared to have an association with hazard of PE. Jointly modeling only lung function and PE onset yielded the most accurate (area under the receiver-operator characteristic curve = 0.75) and precise (narrowest interquartile range) predictions. Dynamic predictions were accurate across forecast horizons (0.5, 1 and 2 years) and precision improved with age. CONCLUSIONS: Including growth markers via multivariate joint models did not yield gains in prediction performance, compared to a univariate joint model with lung function. Individualized dynamic predictions from joint modeling could enhance physician monitoring of CF disease progression by providing PE risk assessment over a patient's clinical course
External Validation of a Dynamic Prediction Model for Upper Limb Function After Stroke
Objective: To externally validate the dynamic prediction model for prediction of upper limb (UL) function 6 months after stroke. The dynamic prediction model has been developed and cross-validated on data from 4 Dutch studies. Design: Data from a prospective Danish cohort study were used to assess prediction accuracy. Setting: A Danish neurorehabilitation hospital. Participants: In this external validation study, follow-up data for 80 patients in the subacute phase after stroke (N=80), mean age 64 (SD11), 43% women, could be obtained. They were assessed at 2 weeks, 3 months, and 6 months after stroke with the Action Research Arm Test (ARAT), Fugl-Meyer Motor Assessment upper limb (FMA), and Shoulder Abduction (SA) Finger Extension (FE), (SAFE) test. Intervention: Not applicable. Main Outcome Measures: Prediction accuracy at 6 months was examined for 3 categories of ARAT (0-57 points): mild (48-57), moderate (23-47), and severe (0-22). Two individual predictions of ARAT scores at ±6 months post-stroke were computed based on, respectively, baseline (2 weeks) and 3 months ARAT, FE, SA values. The absolute individual differences between observed and predicted ARAT scores were summarized. Results: The prediction model performed best for patients with relatively good UL motor function, with an absolute error median (IQR) of 3 (2-9), and worst for patients with severe UL impairment, with a median (IQR) of 30 (3-39) at baseline. In general, prediction accuracy substantially improved when data obtained 3 months after stroke was included compared with baseline at 2 weeks after stroke. Conclusion: We found limited clinical usability due to the lack of prediction accuracy 2 weeks after stroke and for patients with severe UL impairments. The dynamic prediction model could probably be refined with data from biomarkers.</p
Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes
Cystic fibrosis is a chronic lung disease requiring frequent lung-function monitoring to track acute respiratory events (pulmonary exacerbations). The association between lung-function trajectory and time-to-first exacerbation can be characterized using joint longitudinal-survival modeling. Joint models specified through the shared parameter framework quantify the strength of association between such outcomes but do not incorporate latent sub-populations reflective of heterogeneous disease progression. Conversely, latent class joint models explicitly postulate the existence of sub-populations but do not directly quantify the strength of association. Furthermore, choosing the optimal number of classes using established metrics like deviance information criterion is computationally intensive in complex models. To overcome these limitations, we integrate latent classes in the shared parameter joint model through a fully Bayesian approach. To choose the optimal number of classes, we construct a mixture model assuming more latent classes than present in the data, thereby asymptotically “emptying” superfluous latent classes, provided the Dirichlet prior on class proportions is sufficiently uninformative. Model properties are evaluated in simulation studies. Application to data from the US Cystic Fibrosis Registry supports the existence of three sub-populations corresponding to lung-function trajectories with high initial forced expiratory volume in 1 s (FEV1), rapid FEV1 decline, and low but steady FEV1 progression. The association between FEV1 and hazard of exacerbation was negative in each class, but magnitude varied
Diffusion weighted imaging in cystic fibrosis disease: beyond morphological imaging
To explore the feasibility of diffusion-weighted imaging (DWI) to assess inflammatory lung changes in patients with Cystic Fibrosis (CF) METHODS: CF patients referred for their annual check-up had spirometry, chest-CT and MRI on the same day. MRI was performed in a 1.5 T scanner with BLADE and EPI-DWI sequences (b = 0-600 s/mm(2)). End-inspiratory and end-expiratory scans were acquired in multi-row scanners. DWI was scored with an established semi-quantitative scoring system. DWI score was correlated to CT sub-scores for bronchiectasis (CF-CTBE), mucus (CF-CTmucus), total score (CF-CTtotal-score), FEV1, and BMI. T-test was used to assess differences between patients with and without DWI-hotspots
Dynamic prediction of outcome for patients with severe aortic stenosis: Application of joint models for longitudinal and time-to-event data
Background: Physicians utilize different types of information to predict patient prognosis. For example: confronted with a new patient suffering from severe aortic stenosis (AS), the cardiologist considers not only the severity of the AS but also patient characteristics, medical history, and markers such as BNP. Intuitively, doctors adjust their prediction of prognosis over time, with the change in clinical status, aortic valve area and BNP at each outpatient clinic visit. With the help of novel statistical approaches to model outcomes, it is now possible t
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