[Use of GEE for modeling censored correlated data: application to the study of risk factors for withdrawal of totally implantable vascular access devices in cystic fibrosis].
BACKGROUND
The proportional hazards model proposed by Cox for modeling censored data is not suited for correlated delays, for instance when several events can be observed on each subject.


METHODS
To analyze correlated delays, we propose to use a log-linear marginal model equivalent to Cox model. Correlations are taken into account through the use of Liang and Zeger's Generalized Estimating Equations (GEE) and of their robust variance estimator. An advantage of this method is that it can be implemented through the SAS GENMOD procedure. When ties are observed, we propose to use multiple imputations, creating M data sets without ties from the original one.


RESULTS
This method is applied to a retrospective survey on the risk of withdrawing totally implantable vascular access devices (TIVAD) because of complication in cystic fibrosis patients: 265 TIVAD implanted in 200 patients were observed. Risk factors were characteristics of the device or of the patient. Results obtained with the robust variance estimator and ten imputations show that the use of the device for taking blood (vs exclusive perfusion of antibiotics), polyurethane catheter (vs. silicon), use of counterpressure for upkeeping and pulmonary colonization by Pseudomonas Aeruginosa are significantly associated to withdrawal. Under the Cox model which does not account for the correlations, some conclusions differ because the robust variance of the estimators is smaller than the variance obtained under the working assumption of independent delays.


CONCLUSIONS
This approach allows the modeling of correlated survival data with SAS software. Our results illustrate the necessity of accounting for existing correlations.
Bloch
J
J
Service de Santé Publique, Hôpital Robert Debré, 48 Bd Serurier 75019 Paris.
Chavance
M
M
Lellouch
J
J
Tahri
N
N
Munck
A
A
Malbezin
S
S
D002408:Catheters, Indwelling / Q000706:statistics & numerical data*; D003550:Cystic Fibrosis / Q000628:therapy*; D006801:Humans; D015233:Models, Statistical; D012307:Risk Factors