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Related Experiment Videos

On the Inclusion of Non-Concurrent Controls in Platform Trials With an Interim Analysis.

Pavla Krotka¹, Martin Posch², Marta Bofill Roig¹

¹Department of Statistics and Operations Research and Institute for Research and Innovation in Health (IRIS), Universitat Politécnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain.

Statistics in Medicine

|May 18, 2026

Summary

This summary is machine-generated.

Platform trials can use non-concurrent controls, but interim analyses may bias results. A new method reduces bias and improves power when using non-concurrent controls with interim analyses in platform trials.

Keywords:

bias adjustment interim analysis non‐concurrent controls platform trials treatment effect estimation

Related Experiment Videos

Area of Science:

Biostatistics
Clinical Trial Design

Background:

Platform trials offer efficient drug development by testing multiple treatments with a shared control arm.
Non-concurrent controls can enhance platform trial analysis but may introduce bias, especially with time trends and interim analyses.

Purpose of the Study:

To evaluate the impact of interim analyses on treatment effect estimation in platform trials using non-concurrent controls.
To propose and assess a novel statistical method for unbiased estimation in such settings.

Main Methods:

A frequentist regression model was adapted for a platform trial with two experimental arms and a delayed second arm, incorporating non-concurrent controls and time adjustments.
The study simulated a platform trial with an interim analysis in the first arm to assess bias in the second arm's treatment effect.

Main Results:

An unadjusted regression model using non-concurrent controls can introduce bias in treatment effect estimation for the second arm following an interim analysis in the first arm.
The proposed novel estimator significantly reduced bias and type I error rate inflation.

Conclusions:

Interim analyses in platform trials with non-concurrent controls require careful methodological consideration to avoid bias.
The newly proposed estimator offers a robust solution, enhancing reliability and power compared to traditional methods.