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Group sequential design for historical control trials using error spending functions.

Jianrong Wu1, Yimei Li2

  • 1Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, KY, USA.

Journal of Biopharmaceutical Statistics
|November 14, 2019
PubMed
Summary
This summary is machine-generated.

Group sequential designs with historical controls are proposed for time-to-event data. These methods maintain statistical accuracy, ensuring reliable results in clinical trials.

Keywords:
Error spending functiondecision boundariesgroup sequential designhistorical controlinformation time

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Historical control trials (HCTs) are increasingly used in clinical research.
  • Adaptive trial designs allow for modifications based on accumulating data.
  • Time-to-event endpoints are common in many therapeutic areas.

Purpose of the Study:

  • To propose group sequential designs for historical control trials.
  • To incorporate Lan-DeMets error spending functions for flexible interim analyses.
  • To develop designs suitable for time-to-event endpoints.

Main Methods:

  • Group sequential methods were adapted for HCTs.
  • Lan-DeMets error spending functions (O'Brien-Fleming and Gamma families) were utilized.
  • Sequential log-rank tests, modeled via Brownian motion, informed boundary derivations.

Main Results:

  • The proposed group sequential designs effectively control Type I error rates.
  • Statistical power is preserved when using historical controls with these designs.
  • Simulation studies validated the performance of the developed methods.

Conclusions:

  • Group sequential designs incorporating Lan-DeMets error spending functions are viable for HCTs with time-to-event outcomes.
  • These methods offer a robust framework for adaptive decision-making in clinical trials.
  • The approach maintains the integrity of statistical inference while leveraging historical data.