Bayesian subset analysis in a colorectal cancer clinical trial

Area of Science:

• Biostatistics
• Clinical Trial Methodology
• Statistical Modeling

Background:

• Subset analyses examine treatment effects in specific patient subgroups.
• These analyses are susceptible to multiplicity issues, inflating Type I error rates.
• Existing methods may not adequately address the complexities of interaction effects in subset analyses.

Purpose of the Study:

• To develop and present a Bayesian statistical method for handling multiplicity in subset analyses.
• To estimate subset-specific treatment effects within a proportional hazards framework.
• To provide a natural approach for discounting interaction effects and managing multiplicity.

Main Methods:

• Utilized a proportional hazards model incorporating treatment, dichotomous covariates, and interaction terms.
• Employed Bayesian estimation with exchangeable priors for interaction terms and locally uniform priors for other parameters.
• Implemented a shrinkage approach for interaction effects towards zero.

Main Results:

• The Bayesian method effectively shrinks estimated interaction effects, mitigating multiplicity concerns.
• Provides point and interval estimates for subset-specific treatment effects.
• Demonstrated the method's utility with a colorectal cancer clinical trial dataset.

Conclusions:

• The proposed Bayesian approach offers a robust solution for multiplicity in subset analyses.
• This method facilitates more reliable interpretation of treatment effects across patient subgroups.
• The findings have implications for the design and analysis of clinical trials, particularly in oncology.