Abstract

The public health burden of non-alcoholic steatohepatitis (NASH), a liver condition characterized by excessive lipid accumulation and subsequent tissue inflammation and fibrosis, has burgeoned with the spread of western lifestyle habits. Progression of fibrosis into cirrhosis is assessed using histological staging scales (e.g., NASH Clinical Research Network (NASH CRN)). These scales are used to monitor disease progression as well as to evaluate the effectiveness of therapies. However, clinical drug trials for NASH are typically underpowered due to lower than expected inter-/intra-rater reliability, which impacts measurements at screening, baseline, and endpoint. Bridge ratings represent a phenomenon where pathologists assign two adjacent stages simultaneously during assessment and may further complicate these analyses when ad hoc procedures are applied. Statistical techniques, dubbed Bridge Category Models, have been developed to account for bridge ratings, but not for the scenario where multiple pathologists assess biopsies across time points. Here, we develop hierarchical Bayesian extensions for these statistical methods to account for repeat observations and use these methods to assess the impact of bridge ratings on the inter-/intra-rater reliability of the NASH CRN staging scale. We also report on how pathologists may differ in their assignment of bridge ratings to highlight different staging practices. Our findings suggest that Bridge Category Models can capture additional fibrosis staging heterogeneity with greater precision, which translates to potentially higher reliability estimates in contrast to the information lost through ad hoc approaches.