A Joint Model for Longitudinal and Time-to-event Data in Social and Life Course Research: Employment Status and Time to Retirement

2021 ◽  
pp. 004912412110557
Author(s):  
Jolien Cremers ◽  
Laust Hvas Mortensen ◽  
Claus Thorn Ekstrøm
Keyword(s):  
Event History Analysis ◽  
Social Research ◽  
Joint Model ◽  
Event History ◽  
Multiple Time ◽  
Time To Event ◽  
Joint Models ◽  
Longitudinal Outcome ◽  
Endogenous Covariates ◽  
Association Structure

Longitudinal studies including a time-to-event outcome in social research often use a form of event history analysis to analyse the influence of time-varying endogenous covariates on the time-to-event outcome. Many standard event history models however assume the covariates of interest to be exogenous and inclusion of an endogenous covariate may lead to bias. Although such bias can be dealt with by using joint models for longitudinal and time-to-event outcomes, these types of models are underused in social research. In order to fill this gap in the social science modelling toolkit, we introduce a novel Bayesian joint model in which a multinomial longitudinal outcome is modelled simultaneously with a time-to-event outcome. The methodological novelty of this model is that it concerns a correlated random effects association structure that includes a multinomial longitudinal outcome. We show the use of the joint model on Danish labour market data and compare the joint model to a standard event history model. The joint model has three advantages over a standard survival model. It decreases bias, allows us to explore the relation between exogenous covariates and the longitudinal outcome and can be flexibly extended with multiple time-to-event and longitudinal outcomes.

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 Joint Models of Longitudinal and Time-to-Event Data with More Than One Event Time Outcome: A Review

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Graeme L. Hickey ◽  
Pete Philipson ◽  
Andrea Jorgensen ◽  
Ruwanthi Kolamunnage-Dona
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Public Health Research ◽  
Single Event ◽  
Time To Event ◽  
Joint Models ◽  
Event Time ◽  
Longitudinal Outcome ◽  
Modelling Assumptions ◽  
Association Structure

AbstractMethodological development and clinical application of joint models of longitudinal and time-to-event outcomes have grown substantially over the past two decades. However, much of this research has concentrated on a single longitudinal outcome and a single event time outcome. In clinical and public health research, patients who are followed up over time may often experience multiple, recurrent, or a succession of clinical events. Models that utilise such multivariate event time outcomes are quite valuable in clinical decision-making. We comprehensively review the literature for implementation of joint models involving more than a single event time per subject. We consider the distributional and modelling assumptions, including the association structure, estimation approaches, software implementations, and clinical applications. Research into this area is proving highly promising, but to-date remains in its infancy.

scholarly journals Characterization of direct and/or indirect genetic associations for multiple traits in longitudinal studies of disease progression

2021 ◽  
Author(s):  
Myriam Brossard ◽  
Andrew D. Paterson ◽  
Osvaldo Espin-Garcia ◽  
Radu V. Craiu ◽  
Shelley B. Bull
Keyword(s):  
Risk Factors ◽  
Disease Progression ◽  
Complex Traits ◽  
Measurement Errors ◽  
Joint Model ◽  
Alternative Methods ◽  
Joint Analysis ◽  
Multiple Time ◽  
Time To Event ◽  
Subject Specific

When quantitative longitudinal traits are risk factors for disease progression, endogenous, and/or subject to random errors, joint model specification of multiple time-to-event and multiple longitudinal traits can effectively identify direct and/or indirect genetic association of single nucleotide polymorphisms (SNPs) with time-to-event traits. Here, we present a joint model that integrates: i) a linear mixed model describing the trajectory of each longitudinal trait as a function of time, SNP effects and subject-specific random effects, and ii) a frailty Cox survival model that depends on SNPs, longitudinal trajectory effects, and a subject-specific frailty term accounting for unexplained dependency between time-to-event traits. Inference is based on a two-stage approach with bootstrap joint covariance estimation. We develop a hypothesis testing procedure to identify direct and/or indirect SNP association with each time-to-event trait. Motivated by complex genetic architecture of type 1 diabetes complications (T1DC) observed in the Diabetes Control and Complications Trial (DCCT), we show by realistic simulation study that joint modelling of two time-to-T1DC (retinopathy, nephropathy) and two longitudinal risk factors (HbA1c, systolic blood pressure) reduces bias and improves identification of direct and/or indirect SNP associations, compared to alternative methods ignoring measurement errors in intermediate risk factors. Through analysis of DCCT, we identify two SNPs with indirect associations with multiple time-to-T1DC traits and obtain similar conclusions using alternative formulations of time-dependent HbA1c effects on T1DC. In total, joint analysis of multiple longitudinal and multiple time-to-event traits provides insight into etiology of complex traits.

scholarly journals Joint model robustness compared with the time-varying covariate Cox model to evaluate the association between a longitudinal marker and a time-to-event endpoint

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Maeregu W. Arisido ◽  
Laura Antolini ◽  
Davide P. Bernasconi ◽  
Maria G. Valsecchi ◽  
Paola Rebora
Keyword(s):  
Stem Cell ◽  
Stem Cell Transplantation ◽  
Prognostic Marker ◽  
Haematopoietic Stem Cell Transplantation ◽  
Cox Model ◽  
Joint Model ◽  
Haematopoietic Stem Cell ◽  
Time Varying ◽  
Time To Event ◽  
Joint Models

Abstract Background The recent progress in medical research generates an increasing interest in the use of longitudinal biomarkers for characterizing the occurrence of an outcome. The present work is motivated by a study, where the objective was to explore the potential of the long pentraxin 3 (PTX3) as a prognostic marker of Acute Graft-versus-Host Disease (GvHD) after haematopoietic stem cell transplantation. Time-varying covariate Cox model was commonly used, despite its limiting assumptions that marker values are constant in time and measured without error. A joint model has been developed as a viable alternative; however, the approach is computationally intensive and requires additional strong assumptions, in which the impacts of their misspecification were not sufficiently studied. Methods We conduct an extensive simulation to clarify relevant assumptions for the understanding of joint models and assessment of its robustness under key model misspecifications. Further, we characterize the extent of bias introduced by the limiting assumptions of the time-varying covariate Cox model and compare its performance with a joint model in various contexts. We then present results of the two approaches to evaluate the potential of PTX3 as a prognostic marker of GvHD after haematopoietic stem cell transplantation. Results Overall, we illustrate that a joint model provides an unbiased estimate of the association between a longitudinal marker and the hazard of an event in the presence of measurement error, showing improvement over the time-varying Cox model. However, a joint model is severely biased when the baseline hazard or the shape of the longitudinal trajectories are misspecified. Both the Cox model and the joint model correctly specified indicated PTX3 as a potential prognostic marker of GvHD, with the joint model providing a higher hazard ratio estimate. Conclusions Joint models are beneficial to investigate the capability of the longitudinal marker to characterize time-to-event endpoint. However, the benefits are strictly linked to the correct specification of the longitudinal marker trajectory and the baseline hazard function, indicating a careful consideration of assumptions to avoid biased estimates.

scholarly journals Analyzing Response Times and Other Types of Time-to-Event Data Using Event History Analysis: A Tool for Mental Chronometry and Cognitive Psychophysiology

i-Perception ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 204166952097867
Author(s):  
Sven Panis ◽  
Filipp Schmidt ◽  
Maximilian P. Wolkersdorfer ◽  
Thomas Schmidt
Keyword(s):  
Discrete Time ◽  
Event History Analysis ◽  
Masked Priming ◽  
Event History ◽  
Event Data ◽  
Time To Event ◽  
Advantages And Disadvantages ◽  
Time To Event Data ◽  
History Analysis ◽  
Speed Accuracy

In this Methods article, we discuss and illustrate a unifying, principled way to analyze response time data from psychological experiments—and all other types of time-to-event data. We advocate the general application of discrete-time event history analysis (EHA) which is a well-established, intuitive longitudinal approach to statistically describe and model the shape of time-to-event distributions. After discussing the theoretical background behind the so-called hazard function of event occurrence in both continuous and discrete time units, we illustrate how to calculate and interpret the descriptive statistics provided by discrete-time EHA using two example data sets (masked priming, visual search). In case of discrimination data, the hazard analysis of response occurrence can be extended with a microlevel speed-accuracy trade-off analysis. We then discuss different approaches for obtaining inferential statistics. We consider the advantages and disadvantages of a principled use of discrete-time EHA for time-to-event data compared to (a) comparing means with analysis of variance, (b) other distributional methods available in the literature such as delta plots and continuous-time EHA methods, and (c) only fitting parametric distributions or computational models to empirical data. We conclude that statistically controlling for the passage of time during data analysis is equally important as experimental control during the design of an experiment, to understand human behavior in our experimental paradigms.

Bayesian design of clinical trials using joint models for longitudinal and time-to-event data

Biostatistics ◽  
2020 ◽  
Author(s):  
Jiawei Xu ◽  
Matthew A Psioda ◽  
Joseph G Ibrahim
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Hazard Ratio ◽  
Type I ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Modeling Framework ◽  
Time To Event Data ◽  
Longitudinal Outcome

Summary Joint models for longitudinal and time-to-event data are increasingly used for the analysis of clinical trial data. However, few methods have been proposed for designing clinical trials using these models. In this article, we develop a Bayesian clinical trial design methodology focused on evaluating the treatment’s effect on the time-to-event endpoint using a flexible trajectory joint model. By incorporating the longitudinal outcome trajectory into the hazard model for the time-to-event endpoint, the joint modeling framework allows for non-proportional hazards (e.g., an increasing hazard ratio over time). Inference for the time-to-event endpoint is based on an average of a time-varying hazard ratio which can be decomposed according to the treatment’s direct effect on the time-to-event endpoint and its indirect effect, mediated through the longitudinal outcome. We propose an approach for sample size determination for a trial such that the design has high power and a well-controlled type I error rate with both operating characteristics defined from a Bayesian perspective. We demonstrate the methodology by designing a breast cancer clinical trial with a primary time-to-event endpoint and where predictive longitudinal outcome measures are also collected periodically during follow-up.

Event History Analysis in Demography

1995 ◽  
Vol 49 (2) ◽  
pp. 355-357
Author(s):  
Johannes Huinink
Keyword(s):  
Event History Analysis ◽  
Event History ◽  
History Analysis

From source-oriented databases to event-history data files: a twelve-step action plan for the analysis of individual and household life histories

1998 ◽  
Vol 10 (1-3) ◽  
pp. 1-9
Author(s):  
Onno Boonstra ◽  
Maarten Panhuysen
Keyword(s):  
Life Histories ◽  
Event History Analysis ◽  
Action Plan ◽  
Event History ◽  
Flat File ◽  
Event History Data ◽  
History Analysis ◽  
Good Technique ◽  
Data Files ◽  
The Way

Population registers are recognised to be a very important source for demographic research, because it enables us to study the lifecourse of individuals as well as households. A very good technique for lifecourse analysis is event history analysis. Unfortunately, there are marked differences in the way the data are available in population registers and the way event history analysis expects them to be. The source-oriented approach of computing historical data calls for a ‘five-file structure’, whereas event history analysis only can handle fiat files. In this article, we suggest a series of twelve steps with which population register data can be transposed from a five-file structured database into a ‘flat file’ event history analysis dataset.

Gender Differences in the Link Between Marital Status and the Risk of Cognitive Impairment: Results From the Korean Longitudinal Study of Aging

2021 ◽  
pp. 009141502110241
Author(s):  
Yujin Kim
Keyword(s):  
Cognitive Impairment ◽  
Longitudinal Study ◽  
Marital Status ◽  
Event History Analysis ◽  
Population Aging ◽  
Event History ◽  
Never Married ◽  
History Analysis ◽  
Married Men ◽  
And Gender

In the context of South Korea, characterized by increasing population aging and a changing family structure, this study examined differences in the risk of cognitive impairment by marital status and investigated whether this association differs by gender. The data were derived from the 2006–2018 Korean Longitudinal Study of Aging. The sample comprised 7,568 respondents aged 45 years or older, who contributed 30,414 person-year observations. Event history analysis was used to predict the odds of cognitive impairment by marital status and gender. Relative to their married counterparts, never-married and divorced people were the most disadvantaged in terms of cognitive health. In addition, the association between marital status and cognitive impairment was much stronger for men than for women. Further, gender-stratified analyses showed that, compared with married men, never-married men had a higher risk of cognitive impairment, but there were no significant effects of marital status for women.

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