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Repeated significance tests on accumulating survival data.

¹The Norwegian Cancer Society and Section of Medical Statistics, University of Oslo, Montebello. eva.skovlund@klinmed.uio.no

Journal of Clinical Epidemiology

|October 20, 1999

Summary

This study compared group sequential methods for clinical trials. The O'Brien-Fleming method is preferred for its higher power and reduced risk of early stopping with survival data.

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Area of Science:

Biostatistics
Clinical Trial Design
Survival Analysis

Background:

Group sequential methods are crucial for efficient clinical trial design, allowing interim analyses.
Performing interim analyses without adjusting significance levels can inflate the true Type I error rate.
Accumulating survival data presents unique challenges for interim analysis adjustments.

Purpose of the Study:

To compare the statistical properties of Pocock and O'Brien-Fleming group sequential methods.
To demonstrate the impact of interim analyses on significance levels without proper adjustments.
To evaluate these methods using survival data, including data from a breast cancer trial.

Main Methods:

Stochastic simulation was employed to compare the two group sequential methods.
Survival times were simulated from exponential distributions and data representative of a breast cancer trial.
The Pocock method (fixed significance level) and O'Brien-Fleming method (increasing significance level) were evaluated.

Main Results:

Unplanned interim analyses without significance level adjustments inflated the true overall significance level from 5% to 14% with five tests.
Both Pocock and O'Brien-Fleming methods successfully maintained the desired overall significance level when applied correctly.
The O'Brien-Fleming method exhibited higher statistical power compared to the Pocock method.

Conclusions:

The O'Brien-Fleming group sequential method is generally preferable due to its superior power.
This method also mitigates the risk of premature trial termination based on immature survival data.
Appropriate adjustment of significance levels is critical when conducting interim analyses in survival studies.

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