Interim decision-making strategies in adaptive designs for population selection considering post-progression survival magnitudes
Ryuji Uozumi, Chikuma Hamada

TL;DR
This paper proposes interim decision-making strategies for adaptive oncology trial designs that incorporate correlated endpoints like PFS and OS, especially considering post-progression survival effects, to improve population selection.
Contribution
It introduces a novel interim decision approach using predictive power with correlated endpoints, accounting for post-progression survival magnitudes in adaptive designs.
Findings
The proposed method effectively selects the correct population in simulations.
Incorporating PFS data improves decision accuracy when PPS influences OS.
The strategy performs well under scenarios with varying post-progression survival effects.
Abstract
The development of targeted therapies, which benefit only a subgroup of patients treated for a given type of cancer, has been extremely attractive to many investigators. Adaptive seamless phase II/III designs in oncology clinical trials with interim analyses for subpopulation selection could be used if pre-defined biomarker hypothesis exists. We consider the interim analysis using time-to-event endpoints, e.g., overall survival (OS) and progression-free survival (PFS), to identify whether the whole population or only the biomarker-positive population should be continued into the subsequent stage, whereas a final decision is based on OS data. In this paper, we propose the interim decision-making strategies in adaptive designs with correlated endpoints, considering post-progression survival (PPS) magnitudes. In our approach, the interim decision is made on the basis of predictive power,…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Cancer Genomics and Diagnostics · Gene expression and cancer classification
