Regression analysis of overdispersed correlated count data with subject specic covariates

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Regression analysis of overdispersed correlated count data with subject specic covariates. / Solis-Trapala, Ivonne L.; Farewell, Vernon T.
In: Statistics in Medicine, Vol. 24, No. 16, 2005, p. 2557-2575.

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

Harvard

Solis-Trapala, IL & Farewell, VT 2005, 'Regression analysis of overdispersed correlated count data with subject specic covariates', Statistics in Medicine, vol. 24, no. 16, pp. 2557-2575. https://doi.org/10.1002/sim.2121

APA

Solis-Trapala, I. L., & Farewell, V. T. (2005). Regression analysis of overdispersed correlated count data with subject specic covariates. Statistics in Medicine, 24(16), 2557-2575. https://doi.org/10.1002/sim.2121

Vancouver

Solis-Trapala IL, Farewell VT. Regression analysis of overdispersed correlated count data with subject specic covariates. Statistics in Medicine. 2005;24(16):2557-2575. doi: 10.1002/sim.2121

Author

Solis-Trapala, Ivonne L. ; Farewell, Vernon T. / Regression analysis of overdispersed correlated count data with subject specic covariates. In: Statistics in Medicine. 2005 ; Vol. 24, No. 16. pp. 2557-2575.

Bibtex

@article{c125364b0d1a452fb38af85c31354c24,

title = "Regression analysis of overdispersed correlated count data with subject specic covariates",

abstract = "A robust likelihood approach for the analysis of overdispersed correlated count data that takes into account cluster varying covariates is proposed. We emphasise two characteristics of the proposed method: That the correlation structure satises the constraints on the second moments and that the estimation of the correlation structure guarantees consistent estimates of the regression coecients. In addition we extend the mean specication to include within- and between-cluster eects. The method is illustrated through the analysis of data from two studies. In the rst study, cross-sectional count data from a randomised controlled trial are analysed to evaluate the ecacy of a communication skills training programme. The second study involves longitudinal count data which represent counts of damaged hand joints in patients with psoriatic arthritis. Motivated by this study, we generalize our model to accommodate for a subpopulation of patients who are not susceptible to the development of damaged hand joints.",

keywords = "generalized estimating equations , marginal model , multivariate negative binomial model , overdispersed correlated count data , subject specific covariates",

author = "Solis-Trapala, {Ivonne L.} and Farewell, {Vernon T.}",

year = "2005",

doi = "10.1002/sim.2121",

language = "English",

volume = "24",

pages = "2557--2575",

journal = "Statistics in Medicine",

issn = "1097-0258",

publisher = "John Wiley and Sons Ltd",

number = "16",

}

RIS

TY - JOUR

T1 - Regression analysis of overdispersed correlated count data with subject specic covariates

AU - Solis-Trapala, Ivonne L.

AU - Farewell, Vernon T.

PY - 2005

Y1 - 2005

N2 - A robust likelihood approach for the analysis of overdispersed correlated count data that takes into account cluster varying covariates is proposed. We emphasise two characteristics of the proposed method: That the correlation structure satises the constraints on the second moments and that the estimation of the correlation structure guarantees consistent estimates of the regression coecients. In addition we extend the mean specication to include within- and between-cluster eects. The method is illustrated through the analysis of data from two studies. In the rst study, cross-sectional count data from a randomised controlled trial are analysed to evaluate the ecacy of a communication skills training programme. The second study involves longitudinal count data which represent counts of damaged hand joints in patients with psoriatic arthritis. Motivated by this study, we generalize our model to accommodate for a subpopulation of patients who are not susceptible to the development of damaged hand joints.

AB - A robust likelihood approach for the analysis of overdispersed correlated count data that takes into account cluster varying covariates is proposed. We emphasise two characteristics of the proposed method: That the correlation structure satises the constraints on the second moments and that the estimation of the correlation structure guarantees consistent estimates of the regression coecients. In addition we extend the mean specication to include within- and between-cluster eects. The method is illustrated through the analysis of data from two studies. In the rst study, cross-sectional count data from a randomised controlled trial are analysed to evaluate the ecacy of a communication skills training programme. The second study involves longitudinal count data which represent counts of damaged hand joints in patients with psoriatic arthritis. Motivated by this study, we generalize our model to accommodate for a subpopulation of patients who are not susceptible to the development of damaged hand joints.

KW - generalized estimating equations

KW - marginal model

KW - multivariate negative binomial model

KW - overdispersed correlated count data

KW - subject specific covariates

U2 - 10.1002/sim.2121

DO - 10.1002/sim.2121

M3 - Journal article

VL - 24

SP - 2557

EP - 2575

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 1097-0258

IS - 16

ER -

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