Hickey, GL

Jorgensen, A

Kolamunnage-Dona, R

Philipson, P

University of Liverpool Repository

Graeme L. Hickey1, Pete Philipson2, Andrea Jorgensen1, Ruwanthi Kolamunnage-Dona11 Department of Biostatistics, University of Liverpool, UK2 Department of Mathematics and Information Sciences, Northumbria University, UKffffff• Joint models of longitudinal and time-to-event data have predominantly focused on a univariate longitudinal measure and a single event time• However, clinical studies are likely to record multiple longitudinal outcomes and/or multiple, recurrent or competing events• Incorporating all sources of data will improve the predictive capability of any model and lead to more informative inferences for the purpose of medical decision-making• Our study objective is to review the literature for implementations of joint modelling for multivariate dataIntroduction SearchAssociation structureCurrent value, Random effects, JLCM, Correlated random effects, Other (function of imputed longitudinal data profile, cumulative effects, time-dependent slopes, lagged effects, previous values, PMM)Random effectsLongitudinal: Multivariate/independent normal, Semi-parametric, Discrete, Multivariate-tFrailty: Log-normal, Gamma, Positive stable lawTime-to-event dataContinuous, Discrete,Right-censored, Interval-censored, Left-censoredEstimation methodMLE, NP-MLE, Bayesian MCMC,Conditional score estimation,Two-stage regression calibration,Generalised estimating equations,Multiple imputationLongitudinal dataContinuous (incl. censored, bounded),Discrete, Ordinal, Combinations oftypes for multivariate models SoftwareC/C++, Fortran, Matlab, S-Plus, R, OpenBUGS, WinBUGS, JAGS, SAS, Stata, MLnLongitudinal submodelLMM, GLMM, NLMM, Item response theory models, Continuation ratio mixed effects, Mixed effects partial proportional odds model, Zero-inflated Beta regressionmodel, Random change-point mixed modelApplicationCancer, Cardiovascular disease, Lung disease, HIV / AIDS, Mental health, Neurodegenerative disease, Renal disease, Hepatic disease, Cognitive function, ITU care, Neurological diseaseTime-to-event submodelPH (Cox [incl. cause-specific + sub-distribution], piecewiseconstant, M-splines, Weibull, Gompertz, restricted cubic spline), Discrete time hazard log-linear, Cure fractions model, Parametric (log-logistic, log-normal, other), Royston-Parmar, Kaplan-Meier, Truncated-geometric distribution-logistic model, Two-step mixture model, Transformation models, Multistate modelsDiagnostics & model comparisonAIC, BIC, DIC, LRT, LPML, CPO, Local influence measures, Case-deletion diagnostics, Graphical methods, Score test, MCMC diagnostics, Multivariate L-measure, Residual analyses (longitudinal, time-to-event), Predictive ability assessment (Harrell’s C-statistic, dynamic AUC, dynamic Brier score), Two-group comparison tests, Measures of relative benefitMultivariate joint modelMultiple longitudinal outcomesCompeting risksMultiple eventsRecurrent eventsMultistate eventsRandom effectsEstimation methodSoftwareApplicationDiagnostics & model comparisonLongitudinal submodelTime-to-event submodelLongitudinal dataTime-to-event dataAssociation structure• Majority of articles only considered either multivariate longitudinal or time-to-event data• Numerous innovations in models, distributional assumptions, estimation methodologies• Diverse range of association structures linking submodels• Limited clinical application, with methodological papers concentrating most on cardiovascular, neurodegenerative, lung, cancer, and HIV/AIDs diseases• A lack of software implementations that allow researchers to easily exploit novel methodology, with R the most reported software used for analyses• A number of novel developments in the field of diagnostics to measure benefit and assess model assumptionsHighlightsE:   graeme.hickey@Liverpool.ac.ukW: https://www.liverpool.ac.uk/translational-medicine/departmentsandgroups/joine-r/about/Funding: Medical Research Council (MRC) UK Grant MR/M013227/1Methods• Search of Ovid MEDLINE (1946 to August 2015)• Keywords included ‘multivariate’, ‘time-to-event’, ‘longitudinal’, etc. • Web search and scan of reference sections• Information extracted on multivariate features, data types, submodels, distributional assumptions, estimation methods, applications, diagnostics, softwareFuture research• Development of the R package joineR to incorporate multivariate outcomes –longitudinal and event times• Explore techniques for overcoming intractable numerical integration with multiple longitudinal outcomesjoineRavailable from CRAN* 3 articles from a single source

A methodological review of joint modelling of multivariate time-to-event data and longitudinal outcomes

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