scholarly journals Marginalized Two-Part Joint Modeling of Longitudinal Semi-Continuous Responses and Survival Data: With Application to Medical Costs

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2603
Author(s):  
Mohadeseh Shojaei Shahrokhabadi ◽  
(Din) Ding-Geng Chen ◽  
Sayed Jamal Mirkamali ◽  
Anoshirvan Kazemnejad ◽  
Farid Zayeri
Keyword(s):  
Survival Data ◽  
Joint Modeling ◽  
Medical Costs ◽  
Joint Model ◽  
Superior Performance ◽  
Parameter Estimates ◽  
Joint Models ◽  
Survival Status ◽  
Continuous Responses ◽  
Zero Values

Non-negative continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical costs data. It is thus critical to incorporate the potential dependence of survival status and longitudinal medical costs in joint modeling, where censorship is death-related. Despite the wide use of conventional two-part joint models (CTJMs) to capture zero-inflation, they are limited to conditional interpretations of the regression coefficients in the model’s continuous part. In this paper, we propose a marginalized two-part joint model (MTJM) to jointly analyze semi-continuous longitudinal costs data and survival data. We compare it to the conventional two-part joint model (CTJM) for handling marginal inferences about covariate effects on average costs. We conducted a series of simulation studies to evaluate the superior performance of the proposed MTJM over the CTJM. To illustrate the applicability of the MTJM, we applied the model to a set of real electronic health record (EHR) data recently collected in Iran. We found that the MTJM yielded a smaller standard error, root-mean-square error of estimates, and AIC value, with unbiased parameter estimates. With this MTJM, we identified a significant positive correlation between costs and survival, which was consistent with the simulation results.

scholarly journals A joint model for longitudinal and survival data based on an AR(1) latent process

2016 ◽  
Vol 27 (5) ◽  
pp. 1285-1311 ◽  
Author(s):  
Silvia Bacci ◽  
Francesco Bartolucci ◽  
Silvia Pandolfi
Keyword(s):  
Survival Data ◽  
Random Effect ◽  
Information Matrix ◽  
Autoregressive Process ◽  
Joint Modeling ◽  
Joint Model ◽  
Parameter Estimates ◽  
Monte Carlo Simulation Study ◽  
Longitudinal Response ◽  
Latent Process

A critical problem in repeated measurement studies is the occurrence of nonignorable missing observations. A common approach to deal with this problem is joint modeling the longitudinal and survival processes for each individual on the basis of a random effect that is usually assumed to be time constant. We relax this hypothesis by introducing time-varying subject-specific random effects that follow a first-order autoregressive process, AR(1). We also adopt a generalized linear model formulation to accommodate for different types of longitudinal response (i.e. continuous, binary, count) and we consider some extended cases, such as counts with excess of zeros and multivariate outcomes at each time occasion. Estimation of the parameters of the resulting joint model is based on the maximization of the likelihood computed by a recursion developed in the hidden Markov literature. This maximization is performed on the basis of a quasi-Newton algorithm that also provides the information matrix and then standard errors for the parameter estimates. The proposed approach is illustrated through a Monte Carlo simulation study and the analysis of certain medical datasets.

scholarly journals Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease

2017 ◽  
Vol 28 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Survival Data ◽  
Functional Data ◽  
Joint Modeling ◽  
Joint Model ◽  
Repeated Measurements ◽  
Joint Models ◽  
Modeling Framework ◽  
Dynamic Prediction

In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects’ future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian functional joint model provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer’s Disease Neuroimaging Initiative study.

scholarly journals A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies

Biostatistics ◽  
2017 ◽  
Vol 19 (3) ◽  
pp. 374-390 ◽  
Author(s):  
Tingting Yu ◽  
Lang Wu ◽  
Peter B Gilbert
Keyword(s):  
Longitudinal Data ◽  
Survival Data ◽  
Joint Model ◽  
Hiv Vaccine ◽  
Likelihood Inference ◽  
Likelihood Method ◽  
Parameter Estimates ◽  
Computationally Efficient ◽  
Approximate Likelihood ◽  
Longitudinal And Survival Data

SUMMARY In HIV vaccine studies, a major research objective is to identify immune response biomarkers measured longitudinally that may be associated with risk of HIV infection. This objective can be assessed via joint modeling of longitudinal and survival data. Joint models for HIV vaccine data are complicated by the following issues: (i) left truncations of some longitudinal data due to lower limits of quantification; (ii) mixed types of longitudinal variables; (iii) measurement errors and missing values in longitudinal measurements; (iv) computational challenges associated with likelihood inference. In this article, we propose a joint model of complex longitudinal and survival data and a computationally efficient method for approximate likelihood inference to address the foregoing issues simultaneously. In particular, our model does not make unverifiable distributional assumptions for truncated values, which is different from methods commonly used in the literature. The parameters are estimated based on the h-likelihood method, which is computationally efficient and offers approximate likelihood inference. Moreover, we propose a new approach to estimate the standard errors of the h-likelihood based parameter estimates by using an adaptive Gauss–Hermite method. Simulation studies show that our methods perform well and are computationally efficient. A comprehensive data analysis is also presented.

scholarly journals Complete Case versus Inverse Probability Weighting Methods of Fitting Incomplete Longitudinal and Survival Data Joint Models

2019 ◽  
pp. 1-10
Author(s):  
D. O. Nyaboga ◽  
A. Mwangi ◽  
D. Lusweti
Keyword(s):  
Missing Data ◽  
Survival Data ◽  
Joint Model ◽  
Information Criteria ◽  
Joint Models ◽  
Full Data ◽  
Complete Case ◽  
Inverse Probability ◽  
Longitudinal And Survival Data ◽  
The Impact

Missing data is a common problem in real word studies especially clinical studies. However, most people working with such data, often drop missing cases from individuals with incomplete observations that occur when patients do not complete the treatment or miss their scheduled visits. This may lead to misleading results and ultimately affect the decision of whether an intervention is good or bad for the patients under treatment. The comparison of Complete Case (CC) and Inverse Probability Weights (IPW) techniques of handling missing data in various models has been addressed, however little has been done to compare these methods when applied to joint models of longitudinal and time to event data. Therefore, this paper seeks to investigate the impact of assuming CC analysis on clinical data with missing cases, comparing it with IPW method when fitting joint models of longitudinal and survival data setting full data model as the baseline model. This paper made use of randomized aids clinical trial data. The model with Deviance Information Criteria (DIC) close to that of full data joint model is considered the best. From the results, joint models from full data, CC and IPW had DIC of 10603.94, 8410.33 and 10600.95 respectively. The joint model obtained from IPW data had a DIC too close to that of full data joint model as compared to model from CC data.

scholarly journals Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset

2019 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
John Paul Clancy ◽  
Rhonda Szczesniak
Keyword(s):  
Cystic Fibrosis ◽  
Lung Function ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Joint Modeling ◽  
Joint Model ◽  
Receiver Operator Characteristic Curve ◽  
Joint Models ◽  
Lung Function Decline ◽  
Growth And Nutrition

Abstract Background: Attenuated decreases in lung function can signal the onset of acute respiratory events known as pulmonary exacerbation (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), percentile measures of body mass index, weight-for-age and height-for-age and PE onset. Relevant demographic/clinical covariates were included in each submodel. 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 < 0.0001), and all had reasonable predictive accuracy (cross-validated area under the receiver-operator characteristic curve > 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. However, the joint-modeling approach itself may be useful for monitoring CF disease progression by providing a means of dynamic risk prediction.

An Overview of Joint Modeling of Time-to-Event and Longitudinal Outcomes

2019 ◽  
Vol 6 (1) ◽  
pp. 223-240 ◽  
Author(s):  
Grigorios Papageorgiou ◽  
Katya Mauff ◽  
Anirudh Tomer ◽  
Dimitris Rizopoulos
Keyword(s):  
Joint Modeling ◽  
Joint Model ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Longitudinal Outcomes ◽  
Estimation Techniques ◽  
Time To Event Data ◽  
Association Structure

In this review, we present an overview of joint models for longitudinal and time-to-event data. We introduce a generalized formulation for the joint model that incorporates multiple longitudinal outcomes of varying types. We focus on extensions for the parametrization of the association structure that links the longitudinal and time-to-event outcomes, estimation techniques, and dynamic predictions. We also outline the software available for the application of these models.

scholarly journals Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset

2020 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
John Paul Clancy ◽  
Rhonda Szczesniak
Keyword(s):  
Cystic Fibrosis ◽  
Lung Function ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Joint Modeling ◽  
Joint Model ◽  
Receiver Operator Characteristic Curve ◽  
Joint Models ◽  
Lung Function Decline ◽  
Growth And Nutrition

Abstract 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 < 0.0001), and all had reasonable predictive accuracy (cross-validated area under the receiver-operator characteristic curve > 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.

scholarly journals Joint Modeling of Repeated Measurements of Different Biomarkers Predicts Mortality in COVID-19 Patients in the Intensive Care Unit.

2021 ◽  
Author(s):  
Kirby Tong-Minh ◽  
Yuri van der Does ◽  
Joost van Rosmalen ◽  
Christian Ramakers ◽  
Diederik Gommers ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Survival Data ◽  
Single Point ◽  
Joint Modeling ◽  
Repeated Measurements ◽  
Joint Models ◽  
Icu Admission ◽  
Risk Of Death ◽  
Increased Risk

Abstract BackgroundPredicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6) and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6 and suPAR on mortality in patients admitted to the ICU with COVID-19. MethodsThis was a retrospective single center cohort study. Patients were included if they tested positive on SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker’s value in a day compared to no change in the same period. ResultsA total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6 and suPAR was significantly predictive of in-hospital mortality with and an HR of 1.523 (1.012 – 6.540), 75.25 (1.116 – 6247) and 24.45 (1.696 – 1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6 and suPAR were 1.117 (1.03 – 1.639), 3.116 (1.029 – 9.963) and 2.319 (1.149 – 6.243) respectively.ConclusionJoint models for the analysis of repeated measurements of PCT, suPAR and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality.

scholarly journals Basic Concepts and Methods for Joint Models of Longitudinal and Survival Data

2010 ◽  
Vol 28 (16) ◽  
pp. 2796-2801 ◽  
Author(s):  
Joseph G. Ibrahim ◽  
Haitao Chu ◽  
Liddy M. Chen
Keyword(s):  
Clinical Trials ◽  
Survival Data ◽  
Eastern Cooperative Oncology Group ◽  
Joint Modeling ◽  
Operating Characteristics ◽  
Joint Models ◽  
Free Survival ◽  
Introductory Overview ◽  
Longitudinal And Survival Data

Joint models for longitudinal and survival data are particularly relevant to many cancer clinical trials and observational studies in which longitudinal biomarkers (eg, circulating tumor cells, immune response to a vaccine, and quality-of-life measurements) may be highly associated with time to event, such as relapse-free survival or overall survival. In this article, we give an introductory overview on joint modeling and present a general discussion of a broad range of issues that arise in the design and analysis of clinical trials using joint models. To demonstrate our points throughout, we present an analysis from the Eastern Cooperative Oncology Group trial E1193, as well as examine some operating characteristics of joint models through simulation studies.

Bayesian Joint Modeling of Skew-Positive Longitudinal-Survival Data Using Birnbaum-Saunders Distribution

2020 ◽  
Author(s):  
Tohid Jafari-Koshki ◽  
Sayed Mohsen Hosseini ◽  
Shahram Arsang-Jang
Keyword(s):  
Survival Data ◽  
Real Data ◽  
Information Criterion ◽  
Joint Modeling ◽  
Multivariate Modeling ◽  
Joint Models ◽  
Wide Range ◽  
Longitudinal Part ◽  
Research Questions ◽  
Longitudinal And Survival Data

Background: There has been a great interest in joint modeling of longitudinal and survival data in recent two decades. Joint models have less restrictive assumptions in multivariate modeling and could address various research questions. This has led to their wide applications in practice. However, earlier models had normality assumption on the distribution in longitudinal part that is usually violated in real data. Hence, recent research have focused on circumventing this issue. Using various skewed distributions has been proposed and applied in the literature. Nevertheless, the flexibility of the proposed methods is limited especially when the data are skew positive. Methods: In this paper, we introduce the use of Birnbaum-Saunders (BS) distribution in joint modeling context. BS distribution is more flexible and could cover a wide range of skew, kurtotic or bimodal data. Results: We analyzed publicly available ddI/ddC data both with normal and BS distributions in Bayesian setting and compared their fit by Widely Applicable Information Criterion (WAIC). The joint BS model showed a better fit to the data. Conclusion: We introduced and applied BS distribution in joint modeling of longitudinal-survival data. Using multi-parameter distributions such as BS in Bayesian setting could improve the fit of models without limitations that arise in transformation of data from original scale. 

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