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The impact of liver transplantation on endpoint selection in alcohol-associated hepatitis trials
Suthat Liangpunsakul1, William B Krebs2, Allison J Kwong3
1Department of Medicine, Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Background:
Alcohol-associated hepatitis (AH) is a serious liver disease caused by heavy alcohol consumption with severe cases exhibiting a 90-day mortality rate of ~30%. No drugs have been approved for AH, and regulatory approval currently requires evidence of improved survival. The lack of effective drug therapies and high mortality rates have fueled interest in early liver transplantation (LT), which has a survival rate that exceeds 90%. However, LT is resource-intensive and is available only in expert centers, where most AH trials are conducted. As a result, LT is overrepresented in recent AH studies, leading to confounding and unresolved questions regarding valid endpoints in therapeutic AH trials.
Methods:
We propose methodological approaches to address the inclusion of LT in AH trials, supported by power calculations and data from the AHFIRM trial, a 300-patient multicenter study completed in late 2023. We demonstrate the impact of effect size, trial size, and statistical methods on trial design and interpretation.
Results:
Effect size plays a crucial role in power calculations. While 90-day survival is the most efficient endpoint, competing risk analysis, primary stratum analysis, and win ratio are valuable tests for assessing the role of LT. The combined endpoint of death or LT is the least efficient method and requires the largest trial population to achieve statistical significance. We recommend using multiple statistical methods with adjustments for multiplicity.
Conclusions:
The adoption of early LT complicates the assessment of new therapies for AH. Statistical methods and endpoints are critical in power calculations and when assessing the efficacy of new therapeutic agents. We recommend mortality as the primary analysis complemented by hierarchical secondary analyses that avoid problems of multiplicity.
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