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In clinical trials, an experimental treatment is sometimes added on to astandard of care or control therapy in multiple treatment phases (e.g.,concomitant and maintenance phases) to improve patient outcomes. When the newregimen provides meaningful benefit over the control therapy in such cases, itproves difficult to separately assess the contribution of each phase to theoverall effect observed. This article provides an approach for assessing theimportance of a specific treatment phase in such a situation through tippingpoint analyses of a time-to-event endpoint usingrank-preserving-structural-failure-time (RPSFT) modeling. A tipping-pointanalysis is commonly used in situations where it is suspected that astatistically significant difference between treatment arms could be a resultof missing or unobserved data instead of a real treatment effect.Rank-preserving-structural-failure-time modeling is an approach for causalinference that is typically used to adjust for treatment switching in clinicaltrials with time to event endpoints. The methodology proposed in this articleis an amalgamation of these two ideas to investigate the contribution of atreatment phase of interest to the effect of a regimen comprising multipletreatment phases. We provide two different variants of the method correspondingto two different effects of interest. We provide two different tipping pointthresholds depending on inferential goals. The proposed approaches aremotivated and illustrated with data from a recently concluded, real-life phase3 cancer clinical trial. We then conclude with several considerations andrecommendations.

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