AI Article Synopsis
- - This research examines why positive treatment effects seen in the first interim analysis of clinical trials may decline in later analyses, focusing on issues like overestimation bias, non-proportional hazards, and varied recruitment.
- - The study analyzed 71 oncology randomized clinical trials, showing that hazard ratios often overestimate treatment effects, especially when fewer events have occurred by the interim analysis.
- - The findings highlight the need to apply adjusted hazard ratios to reduce overestimation bias and stress the importance of considering various factors when reporting on positive interim analysis results.
Article Abstract
Background: This research investigates why a beneficial treatment effect reported at the first interim analysis (IA) may diminish at a subsequent analysis (SA). We examined three challenges in interpreting treatment effects from randomized clinical trials (RCTs) after the first positive IA: overestimation bias; non-proportional hazards; and heterogeneity in recruitment. We investigate how a penalized estimation method can address overestimation bias, and discuss additional factors to consider when interpreting positive IA results.
Methods: We identified oncology RCTs reporting positive results at the initial IA and a SA for event-free (EFS) and overall survival (OS). We modeled: (1) the hazard ratio at IA (HR) versus its timing as measured by the information fraction (IF; i.e., events at IA versus total events sought); and (2), the ratio of HR to HR (rHR) versus the IF. This was repeated for HR adjusted for overestimation bias. Examples of the other two challenges were sought.
Results: Amongst 71 RCTs, HR were positively associated with the IF (slope: EFS 0.83, 95 % CI 0.44-1.22; OS 0.25, 95 % CI 0.10-0.41). HR tended to exaggerate HR, and more so the lower the IF (slope rHR versus IF: EFS 0.10, 95 % CI - 0.22 to 0.42; OS 0.26, 95 % CI 0.07-0.46). Adjusted HR did not exaggerate HR (slope rHR versus IF: EFS - 0.14, 95 % CI - 0.67 to 0.39; OS 0.02, 95 % CI - 0.26 to 0.30). Examples of two other challenges are shown.
Conclusion: Overestimation bias, non-proportional hazards, and heterogeneity in recruitment and other important treatments should be considered when communicating estimates of treatment effects from positive IAs.
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| http://dx.doi.org/10.1016/j.ejca.2024.114230 | DOI Listing |
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