Evaluation of operational chronic infection endpoints for HCV vaccine trials

Abstract

Hepatitis C virus (HCV) is a leading cause of chronic liver disease. The natural history of HCV infection is heterogeneous, and a person infected with HCV can clear the virus or progress to a chronic infection. The chronic infection can remain asymptomatic for decades before the development of liver cirrhosis and/or carcinoma. Currently, there are no assays that can differentiate a transient infection (an acute infection that would clear) from a chronic infection, and serial HCV RNA testing is used to operationally define chronic hepatitis C (e.g. detectable HCV over 6 months). Therefore, HCV vaccine trial planning can benefit from the assessment of the endpoint candidates that are aimed at the chronic infection. Operationally defined endpoints based on the virological tests at study visits have been previously studied in the context of human papillomavirus (HPV) vaccine trials. However, HCV natural history is different from HPV, requiring separate considerations. In this work, several definitions of chronic infection that are based on the periodically observed HCV RNA statuses are evaluated, using a multi-state, time-homogeneous Markov model for transient and chronic infections under various infection settings. Our results show some inflation in the type I error in the log-rank test on the vaccine efficacy against chronic infections in the presence of vaccine efficacy related to transient infections. A type I error up to almost four times the planned rate of 5% is observed in one setting. Overall, simple operational endpoints yield higher power than more complex endpoints, but the simplest endpoint is most affected by the type I error inflation and misclassification error due to the assay imperfection.