Confidence bands for multiplicative hazards models: Flexible resampling approaches.

Area of Science:

• Biostatistics
• Survival Analysis
• Statistical Modeling

Background:

• Multistate Cox models are crucial for analyzing complex event histories.
• Accurate confidence bands for cumulative hazard functions are essential for reliable statistical inference.
• Existing methods may lack validity under certain data conditions like time-dependent covariates.

Purpose of the Study:

• To introduce novel resampling-based techniques for constructing time-simultaneous confidence bands.
• To ensure asymptotic validity of these confidence bands in multistate Cox models.
• To demonstrate the practical application and performance of the proposed methods.

Main Methods:

• Development of resampling strategies tailored for cumulative hazard estimation.
• Application of the methodology to the Cox model with time-dependent covariates.
• Inclusion of handling for right-censoring and left-truncation in survival data.

Main Results:

• The proposed resampling methods yield asymptotically valid confidence bands.
• Simulation studies confirm the good finite sample performance of the new approaches.
• Empirical analyses demonstrate successful application to real-world survival and competing risks data.

Conclusions:

• The developed methods offer a robust tool for constructing confidence bands in multistate survival analysis.
• These techniques enhance the reliability of statistical inference for cumulative hazard functions.
• The study provides practical solutions for analyzing complex survival data with censoring and truncation.