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. 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. (C) 2000 by American Society for Reproductive Medicine.