Robust inference for responder analysis: Innovative clinical trial design using a minimum p-value approach

Area of Science:

• Clinical Trials Methodology
• Biostatistics
• Pharmaceutical Research

Background:

• Responder analysis is widely used in clinical trials, particularly with subjective endpoints like rating scales, due to intuitive interpretation.
• However, dichotomizing outcomes reduces study power and results are sensitive to arbitrary cutoff points, lacking consensus in many disease areas.

Purpose of the Study:

• To address the inefficiencies and cutoff-dependency issues in traditional responder analysis.
• To propose a statistically rigorous method that enhances the power and robustness of responder analysis in clinical trials.

Main Methods:

• A novel clinical trial design is proposed, pre-specifying multiple tests for responder rates across a range of cutoffs.
• Formal inference uses the minimum p-value from these tests, with critical values derived from permutation distributions.
• Simulation studies were conducted to evaluate the performance of the proposed method.

Main Results:

• The novel method significantly improves the statistical power of responder analysis compared to traditional approaches.
• In some scenarios, the power approaches that achieved by analyzing the original continuous or ordinal data.
• The approach offers a robust way to explore various responder cutoffs when consensus is lacking.

Conclusions:

• The proposed responder analysis method offers a statistically sound and more powerful alternative to traditional dichotomized outcomes in clinical trials.
• This approach enhances the ability to detect treatment effects and provides flexibility in defining response criteria.
• It represents a significant advancement in the statistical analysis of clinical trial data, particularly for subjective endpoints.