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This summary is machine-generated.

This study introduces new frequentist models to reduce bias when using non-concurrent control (NCC) data in platform trials. The period-adjustment model is most robust for time trends when NCC data is included.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmaceutical Research

Background:

  • Platform trials increasingly use non-concurrent control (NCC) data for efficiency.
  • Incorporating NCC data can improve statistical power and reduce sample size requirements.
  • Temporal drifts in NCC data can introduce bias into effect estimators.

Purpose of the Study:

  • To propose and evaluate frequentist model-based approaches for analyzing late-entering arms using NCC data.
  • To mitigate potential bias introduced by temporal drifts when using NCC data.
  • To adjust for time-varying effects within platform trial analyses.

Main Methods:

  • Proposed two extensions to existing time-adjustment models for NCC data.
  • Investigated fixed-length calendar time intervals and alternative model-based adjustments (random effects, polynomial splines).
  • Evaluated performance via simulation studies and a case study.

Main Results:

  • Spline-based time adjustment controlled type I error for smooth time trends and offered power gains.
  • The fixed-effect model with period adjustment demonstrated robustness for arbitrary time trends, assuming equality across arms.
  • Period-adjusted models are preferred for trials with sudden time trend changes when using NCC data.

Conclusions:

  • New frequentist models effectively leverage NCC data while adjusting for temporal effects in platform trials.
  • Model choice depends on the nature of temporal trends and data characteristics.
  • The period-adjustment model offers a robust solution for handling time-varying effects with NCC data, particularly in complex scenarios.