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Hierarchical logistic regression models for clustered binary outcomes in studies of IVF-ET
1Brown University School of Medicine, Providence, Rhode Island 02912, USA. jhogan@stat.brown.edu
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.
Design:
Observational study.
Setting:
Academic research environment.
Patient(S):
Women undergoing IVF-ET for tubal disease.
Main Outcome Measure(S):
Odds of per embryo implantation.
Result(S):
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.
Conclusion(S):
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.