scholarly journals New approaches for censored longitudinal data in joint modelling of longitudinal and survival data, with application to HIV vaccine studies

2018 ◽  
Vol 25 (2) ◽  
pp. 229-258 ◽  
Author(s):  
Tingting Yu ◽  
Lang Wu ◽  
Peter Gilbert
Keyword(s):  
Longitudinal Data ◽  
Survival Data ◽  
Hiv Vaccine ◽  
Joint Modelling ◽  
New Approaches ◽  
Longitudinal And Survival Data

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 Revisiting Methods For Modeling Longitudinal and Survival Data: The Framingham Heart Study

2021 ◽  
Author(s):  
Julius S Ngwa ◽  
Howard J Cabral ◽  
Debbie M Cheng ◽  
David R Gagnon ◽  
Michael P LaValley ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Longitudinal Data ◽  
Survival Data ◽  
Framingham Heart Study ◽  
Time Dependent ◽  
Time To Event ◽  
Longitudinal Measure ◽  
Heart Study ◽  
Longitudinal And Survival Data ◽  
Joint Method

Abstract Background: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.Conclusions: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

scholarly journals Flexible parametric joint modelling of longitudinal and survival data

2012 ◽  
Vol 31 (30) ◽  
pp. 4456-4471 ◽  
Author(s):  
Michael J. Crowther ◽  
Keith R. Abrams ◽  
Paul C. Lambert
Keyword(s):  
Survival Data ◽  
Joint Modelling ◽  
Longitudinal And Survival Data

scholarly journals Revisiting Methods For Modeling Longitudinal and Survival Data: The Framingham Heart Study

2020 ◽  
Author(s):  
Julius S Ngwa ◽  
Howard J Cabral ◽  
Debbie M Cheng ◽  
David R Gagnon ◽  
Michael P LaValley ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Longitudinal Data ◽  
Survival Data ◽  
Framingham Heart Study ◽  
Time Dependent ◽  
Time To Event ◽  
Longitudinal Measure ◽  
Heart Study ◽  
Longitudinal And Survival Data ◽  
Joint Method

Abstract Background Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results Simulation results demonstrate that Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years. Conclusions Traditional methods for modeling longitudinal and survival data, such as time dependent covariate method, that use the observed longitudinal data, tend to provide downward bias estimates. Two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

scholarly journals Assessing importance of biomarkers: A Bayesian joint modelling approach of longitudinal and survival data with semi-competing risks

2020 ◽  
pp. 1471082X2093336
Author(s):  
Fan Zhang ◽  
Ming-Hui Chen ◽  
Xiuyu Julie Cong ◽  
Qingxia Chen
Keyword(s):  
Disease Progression ◽  
Competing Risks ◽  
Survival Data ◽  
Marginal Likelihood ◽  
Cox Proportional Hazards Model ◽  
Terminal Event ◽  
Joint Modelling ◽  
Patient Reported ◽  
Longitudinal And Survival Data ◽  
Time Varying Covariates

Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modelling of PRO/QOL and survival data can provide a comparative assessment of patient-reported changes in specific symptoms or global measures that correspond to changes in survival. Motivated by a head and neck cancer clinical trial, we develop a class of trajectory-based models for longitudinal and survival data with disease progression. Specifically, we propose a class of mixed effects regression models for longitudinal measures, a cure rate model for the disease progression time ([Formula: see text]) and a Cox proportional hazards model with time-varying covariates for the overall survival time ([Formula: see text]) to account for [Formula: see text] and treatment switching. Under the semi-competing risks framework, the disease progression is the non-terminal event, the occurrence of which is subject to a terminal event of death. The properties of the proposed models are examined in detail. Within the Bayesian paradigm, we derive the decompositions of the deviance information criterion (DIC) and the logarithm of the pseudo-marginal likelihood (LPML) to assess the fit of the longitudinal component of the model and the fit of each survival component, separately. We further develop [Formula: see text]DIC as well as [Formula: see text]LPML to determine the importance and contribution of the longitudinal data to the model fit of the [Formula: see text] and [Formula: see text] data.

scholarly journals Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Julius S. Ngwa ◽  
Howard J. Cabral ◽  
Debbie M. Cheng ◽  
David R. Gagnon ◽  
Michael P. LaValley ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Longitudinal Data ◽  
Survival Data ◽  
Framingham Heart Study ◽  
Time Dependent ◽  
Time To Event ◽  
Longitudinal Measure ◽  
Heart Study ◽  
Longitudinal And Survival Data ◽  
Joint Method

Abstract Background Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years. Conclusions Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

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