Bootstrap methods for statistical inference from stereological estimates of volume fraction

Area of Science:

• Quantitative pathology
• Biostatistics
• Computational biology

Background:

• Stereological estimation of volume fraction is crucial in pathology for quantifying tissue composition.
• Traditional statistical methods may have limitations with complex biological data distributions.
• Bootstrap methods offer a non-parametric, resampling-based approach to statistical inference.

Purpose of the Study:

• To introduce and evaluate bootstrap methods for stereological volume fraction estimation.
• To assess the performance of bootstrap techniques in calculating confidence intervals and testing differences.
• To demonstrate the application of bootstrap methods using real-world tumor pathology data.

Main Methods:

• Application of bootstrap resampling for estimating confidence intervals of volume fractions.
• Utilizing bootstrap methods for significance testing between two sample volume fractions.
• Consideration of two sampling designs: independent replicated samples and multiple independent objects.
• Worked examples using real data from mammary and pancreatic cancer pathology.

Main Results:

• Bootstrap confidence intervals for volume fractions were generally narrower than parametric intervals.
• Significance test outcomes using bootstrap methods were consistent with classical parametric tests.
• The reproducibility of the bootstrap technique was confirmed through replicated trials.

Conclusions:

• Bootstrap methods are a robust and reliable tool for inference from stereological volume fraction estimates.
• These methods provide accurate confidence intervals and significance testing, comparable to traditional approaches.
• The study validates the utility of bootstrap techniques in quantitative pathology, particularly in cancer research.