Hierarchical logistic regression models for clustered binary outcomes in studies of IVF-ET

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J W Hogan¹, A S Blazar

¹Brown University School of Medicine, Providence, Rhode Island 02912, USA. jhogan@stat.brown.edu

Fertility and Sterility

|February 26, 2000

Abstract

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.

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