Biostatistics Journal Club: Use of Area Under Curve to Assess Treatment Effects

This talk will focus on the importance of identifying clinically interpretable endpoints in comparative studies, highlighting the challenges that arise when defining treatment contrast measures. The rationale for using the area under the curve (AUC) as a model-free measure will be presented, along with its benefits as an aggregate summary that is independent of specific modeling assumptions that allows for straightforward clinical interpretation. Several applications of AUC-based quantifiers will also be discussed.

Required Reading:

• Moving Beyond the Hazard Ratio in Quantifying the Between-Group Difference in Survival Analysis
• Practical Recommendations on Quantifying and Interpreting Treatment Effects in the Presence of Terminal Competing Risks: A Review
• Quantifying Treatment Effects in Trials with Multiple Event-Time Outcomes
• Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies

Speaker:

LJ Wei, PhD, Harvard T.H. Chan School of Public Health