P10 Bayesian two-step procedures to estimate longitudinal hazard ratio changes for immuno-oncology drugs

Given non-proportionality of hazards (NPH) problems in immuno-oncology, the use of alternative testing methods to unweighted log-rank test (LRT) has been actively discussed. Although some methods can be more powerful than LRT in NPH situations, the problem is that sizing for these alternatives needs a correct specification of the NPH structure. Almost all the phase 3 programs for immuno-oncology products do not have enough confidents about longitudinal changes on hazard ratio (HR). Hence, the use of alternative testing methods would become under- or over-powered study under mis-specified NPH structures.
In this article, we motivate an actual phase 3 trial evaluating a PD-L1 inhibitor. At the designing stage, the trial sponsor had historical datasets of past completed phase 3 trials which evaluated similar immune-checkpoint inhibitors for similar patient entities. We propose Bayesian two-step approach for estimating longitudinal changes on HR by using these available historical datasets.
This approach can estimate the number of the change-points in a data-dependent manner using reversible jump Markov Chain Monte Carlo algorithm [1]. We will introduce several simulation results and inform how the analysis results can be used when designing a current phase 3 trial.

References
[1] Green P. Reversible jump Markov chain monte carlo computation and Bayesian model determination. Biometrika. 1996;82(4):711-732.

Oncology

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