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Joint modelling of longitudinal measurements and event time data.

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Joint modelling of longitudinal measurements and event time data. / Henderson, Robin; Diggle, Peter J.; Dobson, Angela.
In: Biostatistics, Vol. 1, No. 4, 12.2000, p. 465-480.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Henderson, R, Diggle, PJ & Dobson, A 2000, 'Joint modelling of longitudinal measurements and event time data.', Biostatistics, vol. 1, no. 4, pp. 465-480. https://doi.org/10.1093/biostatistics/1.4.465

APA

Henderson, R., Diggle, P. J., & Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data. Biostatistics, 1(4), 465-480. https://doi.org/10.1093/biostatistics/1.4.465

Vancouver

Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000 Dec;1(4):465-480. doi: 10.1093/biostatistics/1.4.465

Author

Henderson, Robin ; Diggle, Peter J. ; Dobson, Angela. / Joint modelling of longitudinal measurements and event time data. In: Biostatistics. 2000 ; Vol. 1, No. 4. pp. 465-480.

Bibtex

@article{e6895234ed384b25ad2c5c50e6052e4e,
title = "Joint modelling of longitudinal measurements and event time data.",
abstract = "This paper formulates a class of models for the joint behaviour of a sequence of longitudinal measurements and an associated sequence of event times, including single-event survival data. This class includes and extends a number of specific models which have been proposed recently, and, in the absence of association, reduces to separate models for the measurements and events based, respectively, on a normal linear model with correlated errors and a semi-parametric proportional hazards or intensity model with frailty. Special cases of the model class are discussed in detail and an estimation procedure which allows the two components to be linked through a latent stochastic process is described. Methods are illustrated using results from a clinical trial into the treatment of schizophrenia.",
keywords = "Biomarkers, Counting processes, Informative drop-out, Repeated measurements, Serial correlation, Survival",
author = "Robin Henderson and Diggle, {Peter J.} and Angela Dobson",
year = "2000",
month = dec,
doi = "10.1093/biostatistics/1.4.465",
language = "English",
volume = "1",
pages = "465--480",
journal = "Biostatistics",
issn = "1468-4357",
publisher = "Oxford University Press",
number = "4",

}

RIS

TY - JOUR

T1 - Joint modelling of longitudinal measurements and event time data.

AU - Henderson, Robin

AU - Diggle, Peter J.

AU - Dobson, Angela

PY - 2000/12

Y1 - 2000/12

N2 - This paper formulates a class of models for the joint behaviour of a sequence of longitudinal measurements and an associated sequence of event times, including single-event survival data. This class includes and extends a number of specific models which have been proposed recently, and, in the absence of association, reduces to separate models for the measurements and events based, respectively, on a normal linear model with correlated errors and a semi-parametric proportional hazards or intensity model with frailty. Special cases of the model class are discussed in detail and an estimation procedure which allows the two components to be linked through a latent stochastic process is described. Methods are illustrated using results from a clinical trial into the treatment of schizophrenia.

AB - This paper formulates a class of models for the joint behaviour of a sequence of longitudinal measurements and an associated sequence of event times, including single-event survival data. This class includes and extends a number of specific models which have been proposed recently, and, in the absence of association, reduces to separate models for the measurements and events based, respectively, on a normal linear model with correlated errors and a semi-parametric proportional hazards or intensity model with frailty. Special cases of the model class are discussed in detail and an estimation procedure which allows the two components to be linked through a latent stochastic process is described. Methods are illustrated using results from a clinical trial into the treatment of schizophrenia.

KW - Biomarkers

KW - Counting processes

KW - Informative drop-out

KW - Repeated measurements

KW - Serial correlation

KW - Survival

U2 - 10.1093/biostatistics/1.4.465

DO - 10.1093/biostatistics/1.4.465

M3 - Journal article

VL - 1

SP - 465

EP - 480

JO - Biostatistics

JF - Biostatistics

SN - 1468-4357

IS - 4

ER -