Statistical Evaluation of Absolute Change versus Responder Analysis in Clinical Trials

Area of Science:

• Clinical Trials Methodology
• Statistical Analysis in Healthcare
• Biostatistics

Background:

• Primary analyses in clinical trials often involve absolute/relative change or responder analyses.
• The optimal choice between these analytical methods remains an open question.
• These methods can differ in sample size, definition of clinical significance, and study outcomes.

Purpose of the Study:

• To compare non-inferiority tests using absolute change versus responder analysis.
• Evaluation focused on sample size, statistical power, and hypothesis testing.
• Identify factors influencing conflicting conclusions between endpoint types.

Main Methods:

• Numerical analysis comparing absolute change endpoint vs. responder analysis.
• Assessment of sample size requirements and statistical power.
• Exploration of the impact of cut-off values, non-inferiority margins, and population distribution.

Main Results:

• Absolute change as an endpoint typically necessitates larger sample sizes.
• Responder analysis demonstrates higher statistical power for a given sample size.
• Conflicting conclusions are more probable with extreme cut-off values and non-normal distributions.

Conclusions:

• Responder analysis is generally more powerful than absolute change, especially with constrained sample sizes.
• Cut-off values and non-inferiority margins significantly influence endpoint comparability.
• Researchers must consider population distribution and cut-off selection to mitigate conflicting results.