| Summary: | Abstract Background Conventional trial design and analysis strategies fail to address the typical challenge of immune-oncology (IO) studies: only a limited percentage of treated patients respond to the experimental treatment. Treating non-responders, we hypothesize, would in part drive non-proportional hazards (NPH) patterns in Kaplan-Meier curves that violates the proportional hazards (PH) assumption required by conventional strategies. Ignoring such violation incurred from treating non-responders in the design and analysis strategy may result in underpowered or even falsely negative studies. Hence, designing innovative IO trials to address such pitfall becomes essential. Methods We empirically tested the hypothesis that treating non-responders in studies of inadequate size is sufficient to cause NPH patterns and thereby proposed a novel strategy, p-embedded, to incorporate the dichotomized response incurred from treating non-responders, as measured by the baseline proportion of responders among treated patients p%, into the design and analysis procedures, aiming to ensure an adequate study power when the PH assumption is violated. Results Empirical studies confirmed the hypothetical cause contributes to the manifestation of NPH patterns. Further evaluations revealed a significant quantitative impact of p% on study efficiency. The p-embedded strategy incorporating the properly pre-specified p% ensures an adequate study power whereas the conventional design ignoring it leads to a severe power loss. Conclusion The p-embedded strategy allows us to quantify the impact of treating non-responders on study efficiency. Implicit in such strategy is the solution to mitigate the occurrence of NPH patterns and enhance the study efficiency for IO trials via enrolling more prospective responders.
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