A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information.

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A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information. / Crouchley, Rob; Davies, R. B.
In: Journal of the Royal Statistical Society: Series A Statistics in Society, Vol. 162, No. 3, 1999, p. 331-347.

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

Harvard

Crouchley, R & Davies, RB 1999, 'A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information.', Journal of the Royal Statistical Society: Series A Statistics in Society, vol. 162, no. 3, pp. 331-347. https://doi.org/10.1111/1467-985X.00139

APA

Crouchley, R., & Davies, R. B. (1999). A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information. Journal of the Royal Statistical Society: Series A Statistics in Society, 162(3), 331-347. https://doi.org/10.1111/1467-985X.00139

Vancouver

Crouchley R, Davies RB. A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information. Journal of the Royal Statistical Society: Series A Statistics in Society. 1999;162(3):331-347. doi: 10.1111/1467-985X.00139

Author

Crouchley, Rob ; Davies, R. B. / A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information. In: Journal of the Royal Statistical Society: Series A Statistics in Society. 1999 ; Vol. 162, No. 3. pp. 331-347.

Bibtex

@article{e57b1dfd1c8741abaa2194593efe5215,

title = "A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information.",

abstract = "The generalized estimating equation (GEE) approach to the analysis of longitudinal data has many attractive robustness properties and can provide a 'population average' characterization of interest, for example, to clinicians who have to treat patients on the basis of their observed characteristics. However, these methods have limitations which restrict their usefulness in both the social and the medical sciences. This conclusion is based on the premise that the main motivations for longitudinal analysis are insight into microlevel dynamics and improved control for omitted or unmeasured variables. We claim that to address these issues a properly formulated random-effects model is required. In addition to a theoretical assessment of some of the issues, we illustrate this by reanalysing data on polyp counts. In this example, the covariates include a base-line outcome, and the effectiveness of the treatment seems to vary by base-line. We compare the random-effects approach with the GEE approach and conclude that the GEE approach is inappropriate for assessing the treatment effects for these data.",

keywords = "Base-line data • Consistency • Count data • Endogenous variables • Generalized estimating equations • Marginal models • Random effects",

author = "Rob Crouchley and Davies, {R. B.}",

year = "1999",

doi = "10.1111/1467-985X.00139",

language = "English",

volume = "162",

pages = "331--347",

journal = "Journal of the Royal Statistical Society: Series A Statistics in Society",

issn = "0964-1998",

publisher = "Wiley",

number = "3",

}

RIS

TY - JOUR

T1 - A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information.

AU - Crouchley, Rob

AU - Davies, R. B.

PY - 1999

Y1 - 1999

N2 - The generalized estimating equation (GEE) approach to the analysis of longitudinal data has many attractive robustness properties and can provide a 'population average' characterization of interest, for example, to clinicians who have to treat patients on the basis of their observed characteristics. However, these methods have limitations which restrict their usefulness in both the social and the medical sciences. This conclusion is based on the premise that the main motivations for longitudinal analysis are insight into microlevel dynamics and improved control for omitted or unmeasured variables. We claim that to address these issues a properly formulated random-effects model is required. In addition to a theoretical assessment of some of the issues, we illustrate this by reanalysing data on polyp counts. In this example, the covariates include a base-line outcome, and the effectiveness of the treatment seems to vary by base-line. We compare the random-effects approach with the GEE approach and conclude that the GEE approach is inappropriate for assessing the treatment effects for these data.

AB - The generalized estimating equation (GEE) approach to the analysis of longitudinal data has many attractive robustness properties and can provide a 'population average' characterization of interest, for example, to clinicians who have to treat patients on the basis of their observed characteristics. However, these methods have limitations which restrict their usefulness in both the social and the medical sciences. This conclusion is based on the premise that the main motivations for longitudinal analysis are insight into microlevel dynamics and improved control for omitted or unmeasured variables. We claim that to address these issues a properly formulated random-effects model is required. In addition to a theoretical assessment of some of the issues, we illustrate this by reanalysing data on polyp counts. In this example, the covariates include a base-line outcome, and the effectiveness of the treatment seems to vary by base-line. We compare the random-effects approach with the GEE approach and conclude that the GEE approach is inappropriate for assessing the treatment effects for these data.

KW - Base-line data • Consistency • Count data • Endogenous variables • Generalized estimating equations • Marginal models • Random effects

U2 - 10.1111/1467-985X.00139

DO - 10.1111/1467-985X.00139

M3 - Journal article

VL - 162

SP - 331

EP - 347

JO - Journal of the Royal Statistical Society: Series A Statistics in Society

JF - Journal of the Royal Statistical Society: Series A Statistics in Society

SN - 0964-1998

IS - 3

ER -

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