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Joint modelling of repeated measurements and time-to-event outcomes: the fourth Armitage lecture.

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Joint modelling of repeated measurements and time-to-event outcomes: the fourth Armitage lecture. / Diggle, Peter J.; Sousa, Inês; Chetwynd, Amanda G.
In: Statistics in Medicine, Vol. 27, No. 16, 20.07.2008, p. 2981-2998.

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Diggle PJ, Sousa I, Chetwynd AG. Joint modelling of repeated measurements and time-to-event outcomes: the fourth Armitage lecture. Statistics in Medicine. 2008 Jul 20;27(16):2981-2998. doi: 10.1002/sim.3131

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Bibtex

@article{75b67cd461b948479196b6dd80564648,
title = "Joint modelling of repeated measurements and time-to-event outcomes: the fourth Armitage lecture.",
abstract = "In many longitudinal studies, the outcomes recorded on each subject include both a sequence of repeated measurements at pre-specified times and the time at which an event of particular interest occurs: for example, death, recurrence of symptoms or drop out from the study. The event time for each subject may be recorded exactly, interval censored or right censored. The term joint modelling refers to the statistical analysis of the resulting data while taking account of any association between the repeated measurement and time-to-event outcomes. In this paper, we first discuss different approaches to joint modelling and argue that the analysis strategy should depend on the scientific focus of the study. We then describe in detail a particularly simple, fully parametric approach. Finally, we use this approach to re-analyse data from a clinical trial of drug therapies for schizophrenic patients, in which the event time is an interval-censored or right-censored time to withdrawal from the study due to adverse side effects.",
keywords = "joint modelling • longitudinal analysis • time to event",
author = "Diggle, {Peter J.} and In{\^e}s Sousa and Chetwynd, {Amanda G.}",
year = "2008",
month = jul,
day = "20",
doi = "10.1002/sim.3131",
language = "English",
volume = "27",
pages = "2981--2998",
journal = "Statistics in Medicine",
issn = "1097-0258",
publisher = "John Wiley and Sons Ltd",
number = "16",

}

RIS

TY - JOUR

T1 - Joint modelling of repeated measurements and time-to-event outcomes: the fourth Armitage lecture.

AU - Diggle, Peter J.

AU - Sousa, Inês

AU - Chetwynd, Amanda G.

PY - 2008/7/20

Y1 - 2008/7/20

N2 - In many longitudinal studies, the outcomes recorded on each subject include both a sequence of repeated measurements at pre-specified times and the time at which an event of particular interest occurs: for example, death, recurrence of symptoms or drop out from the study. The event time for each subject may be recorded exactly, interval censored or right censored. The term joint modelling refers to the statistical analysis of the resulting data while taking account of any association between the repeated measurement and time-to-event outcomes. In this paper, we first discuss different approaches to joint modelling and argue that the analysis strategy should depend on the scientific focus of the study. We then describe in detail a particularly simple, fully parametric approach. Finally, we use this approach to re-analyse data from a clinical trial of drug therapies for schizophrenic patients, in which the event time is an interval-censored or right-censored time to withdrawal from the study due to adverse side effects.

AB - In many longitudinal studies, the outcomes recorded on each subject include both a sequence of repeated measurements at pre-specified times and the time at which an event of particular interest occurs: for example, death, recurrence of symptoms or drop out from the study. The event time for each subject may be recorded exactly, interval censored or right censored. The term joint modelling refers to the statistical analysis of the resulting data while taking account of any association between the repeated measurement and time-to-event outcomes. In this paper, we first discuss different approaches to joint modelling and argue that the analysis strategy should depend on the scientific focus of the study. We then describe in detail a particularly simple, fully parametric approach. Finally, we use this approach to re-analyse data from a clinical trial of drug therapies for schizophrenic patients, in which the event time is an interval-censored or right-censored time to withdrawal from the study due to adverse side effects.

KW - joint modelling • longitudinal analysis • time to event

U2 - 10.1002/sim.3131

DO - 10.1002/sim.3131

M3 - Journal article

VL - 27

SP - 2981

EP - 2998

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 1097-0258

IS - 16

ER -