Joint modelling of longitudinal and competing risks data

Williamson, Paula; Kolamunnage‐Dona, Ruwanthi; Philipson, Pete; Marson, Anthony G

doi:10.1002/sim.3451

Cited by 83 publications

(109 citation statements)

References 26 publications

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“…A few authors have already proposed a joint modeling of longitudinal and multivariate survival data [31,33,38]. Our proposed approach differs from those in two main points.…”

Section: Discussionmentioning

confidence: 99%

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Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data

Deslandes

Chevret

2010

BMC Med Res Methodol

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BackgroundJoint modeling of longitudinal and survival data has been increasingly considered in clinical trials, notably in cancer and AIDS. In critically ill patients admitted to an intensive care unit (ICU), such models also appear to be of interest in the investigation of the effect of treatment on severity scores due to the likely association between the longitudinal score and the dropout process, either caused by deaths or live discharges from the ICU. However, in this competing risk setting, only cause-specific hazard sub-models for the multiple failure types data have been used.MethodsWe propose a joint model that consists of a linear mixed effects submodel for the longitudinal outcome, and a proportional subdistribution hazards submodel for the competing risks survival data, linked together by latent random effects. We use Markov chain Monte Carlo technique of Gibbs sampling to estimate the joint posterior distribution of the unknown parameters of the model. The proposed method is studied and compared to joint model with cause-specific hazards submodel in simulations and applied to a data set that consisted of repeated measurements of severity score and time of discharge and death for 1,401 ICU patients.ResultsTime by treatment interaction was observed on the evolution of the mean SOFA score when ignoring potentially informative dropouts due to ICU deaths and live discharges from the ICU. In contrast, this was no longer significant when modeling the cause-specific hazards of informative dropouts. Such a time by treatment interaction persisted together with an evidence of treatment effect on the hazard of death when modeling dropout processes through the use of the Fine and Gray model for sub-distribution hazards.ConclusionsIn the joint modeling of competing risks with longitudinal response, differences in the handling of competing risk outcomes appear to translate into the estimated difference in treatment effect on the longitudinal outcome. Such a modeling strategy should be carefully defined prior to analysis.

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“…A few authors have already proposed a joint modeling of longitudinal and multivariate survival data [31,33,38]. Our proposed approach differs from those in two main points.…”

Section: Discussionmentioning

confidence: 99%

“…Following previous reports [33,42], random slope and random-intercept and -slope models were considered, namely W 1 (t) = U 1 t or W 1 (t) = U 0 + U 1 t , where ( U 0 , U 1 ) are zero-mean bivariate Gaussian variables.…”

Section: Methodsmentioning

confidence: 99%

Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data

Deslandes

Chevret

2010

BMC Med Res Methodol

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“…Fortunately this has recently changed and several packages and procedures are already available. Three R (R Core Team 2018) packages; JM (Rizopoulos 2010), joineR (Williamson, Kolamunnage-Dona, Philipson, and Marson 2008;Philipson, Diggle, Sousa, Kolamunnage-Dona, Williamson, and Henderson 2018) and lcmm (Proust-Lima, Philipps, Diakite, and Liquet 2017a; Proust-Lima, Philipps, and Liquet 2017b); and one Stata (Stata Corp 2014) module, stjm (Crowther 2012), fit these models using maximum likelihood whereas the R package JMBayes (Rizopoulos 2016(Rizopoulos , 2017 produces Markov chain Monte Carlo simulations to approach this problem from a Bayesian perspective. All these software packages are limited to normal longitudinal data with the exception of the JMBayes R package that allows user-defined likelihood functions for the longitudinal data.…”

Section: Introductionmentioning

confidence: 99%

`%JM`: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses

García-Hernández¹,

Rizopoulos²

2018

J. Stat. Soft.

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In clinical research subjects are usually observed during a period of time. Primary and secondary endpoints are often either responses measured longitudinally over time or the time at which an event of interest occurs. Joint modeling is increasingly being used for multiple purposes such as to adjust the analysis of the longitudinal response for informative dropout mechanisms.In this paper we present %JM, a SAS macro that fits jointly generalized mixed models for longitudinal data and proportional hazards models for time-to-event responses. The macro fits normal, binary, binomial and Poisson longitudinal responses and allows choosing among a range of options to fit the trajectories of the longitudinal response over time: a linear function, splines, natural cubic splines and B-splines. For the time-to-event response, that might be right-censored, the macro fits parametric, stratified or not, proportional hazards models with the following baseline risk functions: exponential, Weibull, piecewise exponential and the generalizations of the Weibull and the Gompertz models based on splines.%JM offers several options to connect the longitudinal model and the time-to-event model: current-value-dependent and slope-dependent shared parameters, lagging effects, cumulative effects, random effects coefficients as shared parameters and interaction effects.

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“…The cumulative incidence for a subject with specific covariate level can then be estimated based on the single model. The joint modeling method has also been extended to the case where there are competing failure time events [2,5,8,15,40]. Cause specific sub-distribution hazard models[10] are used to model the competing survival outcomes in all these proposed methods.…”

Section: Introductionmentioning

confidence: 99%

Joint Modeling of Repeated Measures and Competing Failure Events in a Study of Chronic Kidney Disease

et al. 2016

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We are motivated by the Chronic Renal Insufficiency Cohort (CRIC) study to identify risk factors for renal progression in patients with chronic kidney diseases. The CRIC study collects two types of renal outcomes: glomerular filtration rate (GFR) estimated annually and end stage renal disease (ESRD). A related outcome of interest is death which is a competing event for ESRD. A joint modeling approach is proposed to model a longitudinal outcome and two competing survival outcomes. We assume multivariate normality on the joint distribution of the longitudinal and survival outcomes. Specifically, a mixed effects model is fit on the longitudinal outcome and a linear model is fit on each survival outcome. The three models are linked together by having the random terms of the mixed effects model as covariates in the survival models. EM algorithm is used to estimate the model parameters and the non-parametric bootstrap is used for variance estimation. A simulation study is designed to compare the proposed method with an approach that models the outcomes sequentially in two steps. We fit the proposed model to the CRIC data and show that the protein-to-creatinine ratio is strongly predictive of both estimated GFR and ESRD but not death.

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Joint modelling of longitudinal and competing risks data

Cited by 83 publications

References 26 publications

Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data

Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data

`%JM`: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses

Joint Modeling of Repeated Measures and Competing Failure Events in a Study of Chronic Kidney Disease

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Joint modelling of longitudinal and competing risks data

Cited by 83 publications

References 26 publications

Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data

Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data

%JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses

Joint Modeling of Repeated Measures and Competing Failure Events in a Study of Chronic Kidney Disease

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`%JM`: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses