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PSI Conference 2018: Innovative approaches to deal with delayed treatment effect, cure fraction and treatment switching in time-to-event data in cancer immuno-therapies

04 June 2018

Delayed treatment effect (DTE), cure fraction (CF) and treatment switching (TS) are known to cause the violation of the proportional hazards assumption required in standard analyses of time-to-event data. We investigated their effect on power under different simulation conditions for various data distributions, durations of the study, accrual periods and analysis methods. We investigated their impact on sample size and finally proposed Bayesian methods that integrate DTE, CF and/or TS at design stage.
Simulations show that power decreases dramatically in the presence of DTE and TS, whereas overpowered situations occur when CF is only encountered in the experimental group. Underpowered situations occur when CF is experienced in both the experimental and control groups. Mixed scenarios are observed when DTE, CF and/or TS are analysed in combination. When DTE, CF and/or TS are considered at design stage and integrated into the sample size calculation, an adjustment in the number of subjects may be required to maintain the targeted power.

Sample size re-estimation and/or dependent censoring analysis methods can be also used. Alternatively, Bayesian methodologies can be proactively implemented at design stage to account for potential DTE, CF and/or TS. In conclusion, omitting DTE, CF and/or TS at the design stage could lead to drastic implications on power and significantly impact the chance to appropriately identify the existence of a potential treatment effect. The implementation of Bayesian methodologies during the design stage is recommended in order to proactively anticipate the effect of DTE, CF and/or TS, allowing for a more efficient design.


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