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This article provides a novel approach to assess the importance of specifictreatment phases within a treatment regimen through tipping point analyses(TPA) of a time-to-event endpoint using rank-preserving-structural-failure-time(RPSFT) modelling. In oncology clinical research, an experimental treatment isoften added to the standard of care therapy in multiple treatment phases toimprove patient outcomes. When the resulting new regimen provides a meaningfulbenefit over standard of care, gaining insights into the contribution of eachtreatment phase becomes important to properly guide clinical practice. Newstatistical approaches are needed since traditional methods are inadequate inanswering such questions. RPSFT modelling is an approach for causal inference,typically used to adjust for treatment switching in randomized clinical trialswith time-to-event endpoints. A tipping-point analysis is commonly used insituations where a statistically significant treatment effect is suspected tobe an artifact of missing or unobserved data rather than a real treatmentdifference. The methodology proposed in this article is an amalgamation ofthese two ideas to investigate the contribution of a specific component of aregimen comprising multiple treatment phases. We provide different variants ofthe method and construct indices of contribution of a treatment phase to theoverall benefit of a regimen that facilitates interpretation of results. Theproposed approaches are illustrated with findings from a recently concluded,real-life phase 3 cancer clinical trial. We conclude with severalconsiderations and recommendations for practical implementation of this newmethodology.

Assessing contribution of treatment phases through tipping point  analyses via counterfactual elicitation using rank preserving structural  failure time models