Analysis of Zero-Inflated Count Data From Clinical Trials With Potential Dropouts

Abstract

Counts of prespecified events are important endpoints for many safety and efficacy clinical trials. The conventional Poisson model might not be ideal due to three potential issues: (1) overdispersion arising from intra-subject correlation, (2) zero inflation when the prespecified event is rare, and (3) missing observations due to early dropouts. Negative binomial (NB), Poisson hurdle (PH), and negative binomial hurdle (NBH) models are more appropriate for overdispersed and/or zero-inflated count data. An offset can be included in these models to adjust for differential exposure duration due to early dropouts. In this article, we propose new link functions for the hurdle part of a PH/NBH model to facilitate testing for zero-inflation and model selection. The proposed link function particularly improves the model fit of a NBH model when an offset is included to adjust for differential exposure. A simulation study is conducted to compare the existing and proposed models, which are then applied to data from two clinical trials to demonstrate application and interpretation of these methods.

Key Words:

• Early dropouts
• Hurdle models
• Offset
• Overdispersion
• Zero inflation

Acknowledgments

We thank the editors and two anonymous referees for their constructive comments and suggestions, which have led to substantial improvement in the revised version of the article.