title: Bootstrap methods for statistical inference from stereological estimates of volume fraction.
abstract: We suggest the use of bootstrap methods for inference from stereological estimates of volume fraction. An informal introduction to stereological estimation of volume fraction and to principles of bootstrap techniques is given. The bootstrap method is a robust computer-intensive resampling technique, based on independent random sampling from a data set with replacement. Bootstrap methods were used to estimate confidence intervals for volume fractions, and to test for a significant difference between estimated volume fractions from two samples. Two sampling designs are considered: independent replicated samples (visual fields) from a single object, and estimates of volume fraction from multiple independent objects. The methods are presented as worked examples on real data sets obtained from tumour pathology (mammary cancer, pancreatic cancer). The volume fraction of glandular lumina per total volume of the epithelial phase was chosen as target parameter. It indicates the degree of glandular differentiation in adenocarcinomas and is estimated as a ratio-of-means statistic with variable denominator within cases. The confidence intervals of the volume fraction estimated by the bootstrap method were slightly narrower than the parametrically calculated confidence intervals for all data sets. The outcomes of significance tests based on the bootstrap technique were unchanged as compared with classical tests based on the assumptions of normality and homoscedasticity of the data. Special attention was paid to the reproducibility of the bootstrap technique in replicated trials on the same data.
authors.affiliation: Department of Pathology, University of Ulm, Oberer Eselberg M23, D-89081 Ulm, Germany. torsten.mattfeldt@medizin.uni-ulm.de
authors.forename: T
authors.initials: T
authors.lastname: Mattfeldt
authors.forename: F
authors.initials: F
authors.lastname: Fleischer
mesh_terms: D001943:Breast Neoplasms / Q000145:classification / Q000473:pathology; D044584:Carcinoma, Ductal / Q000145:classification / Q000473:pathology; D018275:Carcinoma, Lobular / Q000145:classification / Q000473:pathology; D021441:Carcinoma, Pancreatic Ductal / Q000145:classification / Q000473:pathology; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D019044:Image Cytometry; D009367:Neoplasm Staging; D010190:Pancreatic Neoplasms / Q000145:classification / Q000473:pathology; D012984:Software; D018709:Statistics, Nonparametric