Hierarchical logistic regression models for clustered binary outcomes in studies of IVF-ET.
OBJECTIVE
To describe a hierarchical logistic regression model for clustered binary data, apply it to data from a study on the effect of hydrosalpinx on embryo implantation, and compare the results with analyses that do not account for clustering.


METHODS
Observational study.


METHODS
Academic research environment.


METHODS
Women undergoing IVF-ET for tubal disease.


METHODS
Odds of per embryo implantation.


RESULTS
Although regression estimates are largely similar between the models, the hierarchical model properly reflects the added variation due to clustering. Standard errors are higher, confidence intervals are wider, and P values indicate fewer "statistically significant" effects.


CONCLUSIONS
Ignoring important sources of variation in any analysis can lead to incorrect confidence intervals and P values. In studies of IVF-ET, where clustered data are common, unexplained heterogeneity can be substantial. In this setting, hierarchical logistic regression is an appropriate alternative to standard logistic regression.
Hogan
J W
JW
Brown University School of Medicine, Providence, Rhode Island 02912, USA. jhogan@stat.brown.edu
Blazar
A S
AS
D000328:Adult; D016000:Cluster Analysis; D010064:Embryo Implantation; D004624:Embryo Transfer; D005184:Fallopian Tube Diseases / Q000150:complications / Q000601:surgery; D005260:Female; D005307:Fertilization in Vitro; D006801:Humans; D007247:Infertility, Female / Q000209:etiology / Q000628:therapy; D016015:Logistic Models; D011247:Pregnancy