Joint Modeling of Survival and Nonignorable Missing Longitudinal Quality-of-Life Data

Dupuy, Jean‐François

doi:10.1007/978-1-4757-3625-0_25

Cited by 9 publications

(26 citation statements)

References 18 publications

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“…When the investigated covariate is internal (Kalbfleisch and Prentice, 1980), conditional analysis is insufficient or incorrect. Dupuy and Mesbah (2002) use a joint model for survival and the longitudinal covariate to estimate the parameters in the Cox model. Identifiability of this joint model, existence and consistency of nonparametric maximum likelihood estimators and asymptotic distribution of the estimators is obtained along with consistent estimator of the asymptotic variance (Dupuy, Grama and Mesbah (2006)).…”

Section: Introductionmentioning

confidence: 99%

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Generalized Estimating Equations

2002

C&H/CRC Monographs on Statistics &Amp; Applied Probability

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In this work, joint analysis of a longitudinal latent multidimensional variable and an event time is considered. The latent variable is measured through a Rasch model using data from questionnaires assessed at several time visits. The responses of our model are two way correlated. First, at a given visit, the responses to questions (items) of a single individual are correlated and second, they are repeated over the visits, they, also become correlated. It is, however, well known that a full likelihood analysis for such mixed models is hampered by the need for numerical integrations. To overcome such integration problems, generalized estimating equations approach is used, following useful approximations. Fixed effects parameters and variance components are estimated consistently by asymptotical normal statistics. A second level of difficulty is the occurrence of death or missing response at dropout time. The likelihood of a global solution is derived, while a full statistical inference is given when the analysis is performed in two separate steps.

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Section: Introductionmentioning

confidence: 99%

“…2006/10/13 file: Article_Singapour_31_12_06.tex date: December 30, 2006 Longitudinal Latent HRQoL and a Survival Process 3 eters. Then the joint model of Dupuy and Mesbah (2002) …”

Section: Introductionmentioning

confidence: 99%

Generalized Estimating Equations

2002

C&H/CRC Monographs on Statistics &Amp; Applied Probability

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“…In these studies, subjects often drop out of the study before the occurrence of the terminal event and the problem of interest then becomes modelling the relationship between the time to dropout and the internal covariate. Dupuy and Mesbah (2002) (DM) proposed a model that described this relationship when the value of the covariate at the dropout time is unobserved. This model combined a first-order Markov model for the longitudinally measured covariate with a time-dependent Cox model for the dropout process.…”

Section: Introductionmentioning

confidence: 99%

“…As pointed out by Dupuy and Mesbah (2002) fitting the Cox model with internal covariates can lead to several problems. The inclusion of a covariate whose path is directly affected by the individual can mask treatment effects when comparing two treatments.…”

Section: Introductionmentioning

confidence: 99%

“…(1995), Wulfsohn and Tsiatis (1997), Dafni and Tsiatis (1998) etc. ) We focus here on the model developed by Dupuy and Mesbah (2002) who considered experiments where it was assumed that each subject leaves the study at a random time T ≥ 0 called the dropout time and objective of the paper was to model the relationship between time to dropout and the longitudinally measured covariate. The work of Dupuy and Mesbah was motivated by a data set concerning quality of life (Qol) of subjects involved in a cancer clinical trial.…”

Section: Introductionmentioning

confidence: 99%

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Goodness of Fit of a joint model for event time and nonignorable missing Longitudinal Quality of Life data

Gulati

Mesbah

Probability, Statistics and Modelling in Public Health

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Abstract:In many survival studies one is interested not only in the duration time to some terminal event, but also in repeated measurements made on a time-dependent covariate. In these studies, subjects often drop out of the study before the occurrence of the terminal event and the problem of interest then becomes modelling the relationship between the time to dropout and the internal covariate. Dupuy and Mesbah (2002) (DM) proposed a model that described this relationship when the value of the covariate at the dropout time is unobserved. This model combined a first-order Markov model for the longitudinally measured covariate with a time-dependent Cox model for the dropout process. Parameters were estimated using the EM algorithm and shown to be consistent and asymptotically normal. In this paper, we propose a test statistic to test the validity of Dupuy and Mesbah's model. Using the techniques developed by Lin (1991), we develop a class of estimators of the regression parameters using weight functions. The test statistic is a function of the standard maximum likelihood estimators and the estimators based on the weight function. Its asymptotic distribution and some related results are presented.

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