Multiplicity in randomised trials II: subgroup and interim analyses.

Area of Science:

• Biostatistics
• Clinical Trial Design
• Medical Research Methodology

Background:

• Subgroup analyses and interim analyses in clinical trials present significant multiplicity concerns.
• Uncontrolled subgroup analyses can lead to spurious findings and distort the medical literature by selectively reporting significant results.
• Repeated interim analyses without proper statistical adjustment escalate the risk of false-positive errors.

Purpose of the Study:

• To address the multiplicity concerns associated with subgroup analyses and interim analyses in clinical trials.
• To recommend appropriate statistical methods for conducting necessary subgroup analyses and interim analyses.
• To highlight the importance of preserving statistical power and controlling the false-positive error rate.

Main Methods:

• Discouraging routine subgroup analyses, advocating for statistical tests of interaction when subgroup investigations are essential.
• Recommending the use of statistical stopping methods, such as O'Brien-Fleming and Peto group sequential methods, for interim analyses.
• Emphasizing the need to account for multiplicity to prevent escalating false-positive error rates.

Main Results:

• Subgroup analyses, if performed, should utilize interaction tests rather than analyzing each subgroup independently.
• Group sequential stopping methods effectively preserve the intended alpha level and statistical power during interim analyses.
• Early termination of trials due to apparent treatment superiority using these methods can lead to exaggerated treatment effect estimates.

Conclusions:

• Subgroup analyses should be approached with caution due to multiplicity concerns; interaction tests are preferred.
• Statistical stopping methods are crucial for managing multiplicity in interim analyses, ensuring trial integrity.
• While early stopping can be beneficial, researchers and readers must be aware of potential overestimation of treatment effects.