Assessing Contribution of Treatment Phases through Tipping Point Analyses via Counterfactual Elicitation Using Rank Preserving Structural Failure Time Models

Abstract

This article provides a novel approach to assess the importance of specific treatment phases within a treatment regimen through tipping point analyses (TPA) of a time-to-event endpoint using rank-preserving-structural-failure-time (RPSFT) modeling. In oncology clinical research, an experimental treatment is often added to the standard of care therapy in multiple treatment phases to improve patient outcomes. When the resulting new regimen provides a meaningful benefit over standard of care, gaining insights into the contribution of each treatment phase becomes important to properly guide clinical practice. New statistical approaches are needed since traditional methods are inadequate in answering such questions. RPSFT modeling is an approach for causal inference, typically used to adjust for treatment switching in randomized clinical trials with time-to-event endpoints. A tipping-point analysis is commonly used in situations where a statistically significant treatment effect is suspected to be an artifact of missing or unobserved data rather than a real treatment difference. The methodology proposed in this article is an amalgamation of these two ideas to investigate the contribution of a specific component of a regimen comprising multiple treatment phases. We provide different variants of the method and construct indices of contribution of a treatment phase to the overall benefit of a regimen that facilitates interpretation of results. The proposed approaches are illustrated with findings from a recently concluded, real-life phase 3 cancer clinical trial. We conclude with several considerations and recommendations for practical implementation of this new methodology.

Keywords:

• Counterfactual elicitation
• Cox regression
• Survival analysis
• Time to event
• Tipping point analysis

Acknowledgments

The authors are deeply thankful to Professor Gary Koch for his review of this research work and suggestions. The work has also greatly benefitted through discussions with and input from fellow research colleagues David Maag (Ph.D.) and Bruce Bach (M.D., Ph.D.).

Disclosure Statement

The authors report there are no competing interests to declare. In particular, no financial or nonfinancial interest has arisen from the direct application of this research work.

Funding

This research work is an independent effort of the authors. No funding is received from any of the organizations for this research.