Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues
TL;DR: A brief overview of joint models for longitudinal and survival data and commonly used methods, including the likelihood method and two-stage methods are provided.
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Abstract: In the past two decades, joint models of longitudinal and survival data have received
much attention in the literature. These models are often desirable in the following situations:
(i) survival models with measurement errors or missing data in time-dependent
covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process
and a longitudinal process are associated via latent variables. In these cases, separate
inferences based on the longitudinal model and the survival model may lead to biased
or inefficient results. In this paper, we provide a brief overview of joint models for
longitudinal and survival data and commonly used methods, including the likelihood
method and two-stage methods.
read more
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MMRM vs joint modeling of longitudinal responses and time to study drug discontinuation in clinical trials using a "de jure" estimand.
TL;DR: A methodology to choose between MMRM and SPM is evaluated consisting of expanding the MMRM density into the likelihood of both longitudinal and time-to-event data by plugging in the likelihood that a parametric TVC model will fit the longitudinal response and time to study drug discontinuation.
6
Predicting COPD Failure by Modeling Hazard in Longitudinal Clinical Data
Jianfei Zhang,Shengrui Wang,Josiane Courteau,Lifei Chen,Aurelien Bach,Alain Vanasse +5 more
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TL;DR: This study proposes a new representation of hazard to capture the relationship between survival probability and time-varying risk factors in a concise but effective way and outperforms the current state-of-the-art prediction models in terms of the survival AUC, concordance index and Birer score metrics.
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•Dissertation
Joint modeling of survival and longitudinal data measured with error, with application to assessing immune correlates of protection in vaccine efficacy trials
Rong Fu
- 01 Jan 2014
TL;DR: Fu et al. as mentioned in this paper adopted the joint modeling framework that models the immune response data measured longitudinally and with error and the time-to-event clinical endpoint simultaneously, and they finally applied the proposed methods to the AIDS Clinical Trials Group (ACTG) 175 dataset, comparing monotherapy with combination therapy among HIV-1-infected subjects.
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Time-to-event data with time-varying biomarkers measured only at study entry, with applications to Alzheimer's disease.
TL;DR: This paper first derive conditions under which using an incorrect time origin of study entry results in consistent estimation of regression parameters when the time-varying covariate is continuous and fully observed, and provides methods for estimating the regression parameter when a functional form can be assumed for thetime-variesing biomarker.
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Joint modeling of longitudinal cholesterol measurements and time to onset of dementia in an elderly African American Cohort
Shanshan Li,Mengjie Zheng,Sujuan Gao +2 more
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TL;DR: It is concluded that, in a healthy cohort of African Americans aged 65 years or more, high late-life cholesterol level is associated with lower incidence of dementia.
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