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Adaptive platform trials can introduce bias with nonconcurrent controls. A new cohort adjustment method, unlike time adjustment, effectively removes this bias, improving trial efficiency and reliability.

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Pharmaceutical Research

Background:

  • Adaptive platform trials enable dynamic treatment arm modifications, potentially leading to control groups being active longer than experimental arms.
  • This results in nonconcurrent controls, which, while offering potential efficiency gains, can introduce bias due to temporal confounding and other factors.
  • Existing methods for controlling confounding in nonconcurrent controls often rely on time period adjustments.

Purpose of the Study:

  • To demonstrate the limitations of time adjustment in mitigating bias from nonconcurrent controls in adaptive platform trials.
  • To propose and validate a more general analytical framework using cohort adjustment to accurately account for nonconcurrent controls.
  • To provide a method for separating randomized and non-randomized treatment effect components.

Main Methods:

  • Defined nonconcurrent controls using the concept of a concurrently randomized cohort.
  • Employed cohort adjustment as an alternative to time adjustment for analyzing data from adaptive platform trials.
  • Utilized simulation studies to compare the performance of time-adjusted versus cohort-adjusted analyses.
  • Interpreted cohort-adjusted analysis through the lens of network meta-analysis for synthesizing randomized and indirect comparisons.

Main Results:

  • Time-adjusted analyses were shown to be insufficient and potentially biased in the presence of nonconcurrent controls.
  • Cohort-adjusted analyses effectively removed bias introduced by nonconcurrent controls.
  • The cohort-adjusted approach allows for the separation and assessment of consistency between randomized and indirect treatment effect components.

Conclusions:

  • Time adjustment is inadequate for preventing bias in adaptive platform trials with nonconcurrent controls.
  • Cohort adjustment provides a robust analytical framework for handling nonconcurrent controls, enhancing the reliability of adaptive platform trials.
  • Presenting separate randomized and indirect contributions to the treatment effect is crucial when nonconcurrent controls are employed.