Bartolucci

Elashoff

Gueorguieva

Henderson

Huang

Keogh

Mauguen

Rizopoulos

Viviani

English

Alessio Farcomeni

Bhuvanesh Pareek

Pulak Ghosh

Crossref

Statistics in Medicine

Discussion on ‘Joint modeling of survival and longitudinal non-survival data’ by Gould <i>et al.</i>

FARCOMENI, Alessio

B. Pareek

P. Ghosh

Discussion on "Joint modeling of survival and longitudinal non-survival data" by Gould et al

Archivio della ricerca- Università di Roma La Sapienza

Discussion on “Joint modeling of survival andlongitudinal non-survival data” by Gould et al.Alessio Farcomeni∗ Bhuvanesh Pareek† Pulak Ghosh‡First, we would like to congratulate the authors on a very accurate andclearly written work. The paper provides a thorough literature review onvarious aspects of the joint model and certainly provides a strong motivationto the use of joint modeling techniques in medical applications.Jointly modelling two or more processes together has been an active areaof research for quite sometime now. This is particularly useful when theprocesses have different distributions, as in mixed data frameworks. A par-ticularly interesting feature is the possibility to predict one event conditionalon the others.We believe three extremely interesting experimental designs arise whichare amenable to joint modeling techniques. First of all, joint models mustbe used when the focus is on the longitudinal outcome(s), but there is infor-mative drop-out. Secondly, joint models must be used when the focus is onthe survival outcome, and there are time-varying covariates which might bemeasured with error [1]. Finally, in many applications both aspects of thejoint model might be of interest, where one might discover that a treatment isbeneficial for survival but detrimental for a longitudinal outcome measuringquality of life or occurrence of a side effect.While much research happened on this topic, it is yet to become popularin the applied field despite the fact that, as surveyed by the authors, thereare now many software options for easy implementation of joint models.Reasons in our opinion range from lack of user-friendly software to lack ofunderstanding of the the advantages of the joint model framework.∗Sapienza - University of Rome†Indian Institute of Management Indore‡Indian Institute of Management Bangalore1Most importantly, editorial (e.g., by referees) pressure to use the jointmodel framework when appropriate in applied and clinical journals seems tobe very light. A quick and non-systematic search on PubMed reveals thatthese models are mentioned mostly only in journals in the areas of statisticsand probability. On the other hand, clinical papers based on longitudinal(observational or randomized) studies are prevalent. A quick skim throughsome reveals that the most common approach is to focus on estimation ofsurvival probabilities based on baseline measurements, ignoring any followingmeasurements. We believe there is a good rationale behind this practice, thatis, one can assess survival probabilities based on the information available atbaseline. On the other hand, ignoring additional measurements is a terriblewaste of information. We must report that we also have found a few papersignoring the problem of informative drop-out and mistreating data as missingat random. These papers report possibly biased results which might havemislead the clinical conclusions and recommendations.We would like to complement the very nice review of Gould et al. bymentioning few additional recent advances.In [2] and [3] it is described how to obtain dynamic predictions of survivalprobabilities. A particularly intriguing feature is that the estimates caneasily be updated after each longitudinal measurement, providing up to dateprognosis assessments.Additionally, many works is this field are based on Gaussian longitudinaloutcomes, but we would like to mention few exceptions that allow to modelsemi-continuous outcomes [4], and quantiles of a continuous outcome [5].Generalized linear models for the outcomes are proposed in [6] from theBayesian perspective and in [7] in a classical framework.Further, in many cases a right-censored survival outcome will be available,but other available options include competing risks as in [8], multiple eventsper subject [9], also considered in the seminal paper of [10], and interval-censored survival outcomes [11, 12].Observation times are commonly not informative (e.g., they might bescheduled in advance). In other situations anyway observation times mightinstead be informative, e.g., patients in worse conditions may be visited moreoften. Joint models with informative observation times have been derived forinstance in [13] and [14].A technical issue in traditional joint models is the use of Gaussian quadra-ture. The use of Gaussian quadrature techniques (e.g., to obtain the expectedcomplete likelihood) in our opinion has often limited the dimensionality of2random effects involved. In many cases only a subject-specific intercept anda random slope are included, which is fine. In our opinion anyway quadraturemay become unstable or too computationally expensive when more randomeffects are specified. A first attempt to tackle this issue can be found in [5],where a general Monte Carlo Expectation Maximization strategy is devel-oped.Another open issue is a full exploration of the flexibility of copulas fordeveloping joint models.Contacting author: Pulak GhoshBannerghatta Road-560076, Indiaemail: pulak.ghosh@iimb.ernet.inReferences[1] Keogh RH, White IR. A toolkit for measurement error correction, with afocus on nutritional epidemiology. Statistics in Medicine 2014; :availableonline.[2] Rizopoulos D. Dynamic predictions and prospective accuracy in jointmodels for longitudinal and time-to-event data. Biometrics 2011;67:819–829.[3] Mauguen A, Rachet B, Mathoulin-Pe´lissier S, MacGrogan G, LaurentA, Rondeau V. Dynamic prediction of risk of death using history ofcancer recurrences in joint frailty models. Statistics in Medicine 2013;32:5366–5380.[4] Liu L. Joint modeling longitudinal semi-continuous data and survival,with application to longitudinal medical cost data. Statistics in Medicine2009; 28:972–986.[5] Farcomeni A, Viviani S. Longitudinal quantile regression in presence ofinformative drop-out through longitudinal-survival joint modeling 2014;:arXiv:1404.1175.[6] Rizopoulos D, Ghosh P. A Bayesian semiparametric multivariate jointmodel for multiple longitudinal outcomes and a time-to-event. Statisticsin Medicine 2011; 30:1366–1380.3[7] Viviani S, Alfo´ M, Rizopoulos D. Generalized linear mixed joint modelfor longitudinal and survival outcomes. Statistics and Computing 2014;24:417–427.[8] Huang X, Li G, Elashoff RM, Pan J. A general joint model for longitudi-nal measurements and competing risks survival data with heterogeneousrandom effects. Lifetime data analysis 2011; 17:80–100.[9] Elashoff RM, Li G, Li N. A joint model for longitudinal measurementsand survival data in the presence of multiple failure types. Biometrics2008; 64:762–771.[10] Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal mea-surements and event time data. Biostatistics 2000; 1:465–480.[11] Gueorguieva R, Rosenheck R, Lin H. Joint modelling of longitudinaloutcome and interval-censored competing risk dropout in a schizophre-nia clinical trial. Journal of the Royal Statistical Society (Series A) 2012;175:417–433.[12] Bartolucci F, Farcomeni A. A discrete time event-history approach toinformative drop-out in mixed latent Markov models with covariates.Biometrics 2014; :in press.[13] Liu L, Huang X, O’Quigley J. Analysis of longitudinal data in thepresence of informative observational times and a dependent terminalevent, with application to medical cost data. Biometrics 2008; 64:950–958.[14] Sun L, Song X, Zhou J, Liu L. Joint analysis of longitudinal data withinformative observation times and a dependent terminal event. Journalof the American Statistical Association 2012; 107:688–700.4

Discussion on "Joint modeling of survival and longitudinal non-survival data"  by Gould et al.

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Discussion on "Joint modeling of survival and longitudinal non-survival data" by Gould et al.

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