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Abstract:
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The presence of various complex survival patterns in immune-oncology (IO) studies violates the Proportional Hazards (PH) assumption required by conventional design and analysis strategies, making most conventionally designed IO studies underpowered or even falsely negative. An efficient study design and analysis strategy should not only salvage the power loss incurred as a result of various complex patterns but also provide a remedy to reduce the occurrence of such patterns. In this paper, we first examine the potential primary root cause behind the complex patterns then present a novel strategy to incorporate the root cause into study design and data analysis, aiming to salvage power loss when the PH assumption is violated. Finite sample efficiency of the proposed methods is evaluated in simulations. Compared with the standard practice based on the regular log-rank test, the proposed design and analysis procedures achieve an adequate study power when various complex patterns are present. In addition, the proposed strategies provide an insight into how the IO study efficiency can be improved.
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