Cox models for incorporating non-concurrent controls in platform trials

P. Krotka, M. Posch, M. Bofill Roig

Platform trials efficiently evaluate multiple treatments, allowing late entry of new arms and sharing controls. For the comparison of late-entering arms, concurrent and non-concurrent controls (NCC) can be used. Using NCC in the analysis can increase power, but may introduce bias due to time trends. To leverage NCC while ensuring valid inference, model-based methods, adjusting for time trends by including a factor period — the intervals bounded by the time points an arm enters or leaves the platform — have been proposed for continuous and binary endpoints.

We extend these approaches to time-to-event endpoints and consider Cox models including the factor period to adjust for time trends. However, a challenge is that survival trials have two timescales: the individual patient time relative to study entry and the calendar time of the patient's recruitment. Through simulations, we assess the type I error rate and power of the proposed Cox model and outline assumptions for valid inference.

Keywords: Platform trials, Non-concurrent controls, Survival analysis, Statistical inference, Statistical modeling

Biostatistics II
June 13, 2025  11:00 AM
MR 3

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