Common Pitfalls in Analysis of Tissue Scores

Abstract

Histopathology remains an important source of descriptive biological data in biomedical research. Recent petitions for enhanced reproducibility in scientific studies have elevated the role of tissue scoring (semiquantitative and quantitative) in research studies. Effective tissue scoring requires appropriate statistical analysis to help validate the group comparisons and give the pathologist confidence in interpreting the data. Each statistical test is typically founded on underlying assumptions regarding the data. If the underlying assumptions of a statistical test do not match the data, then these tests can lead to increased risk of erroneous interpretations of the data. The choice of appropriate statistical test is influenced by the study’s experimental design and resultant data (eg, paired vs unpaired, normality, number of groups, etc). Here, we identify 3 common pitfalls in the analysis of tissue scores: shopping for significance, overuse of paired t-tests, and misguided analysis of multiple groups. Finally, we encourage pathologists to use the full breadth of resources available to them, such as using statistical software, reading key publications about statistical approaches, and identifying a statistician to serve as a collaborator on the multidisciplinary research team. These collective resources can be helpful in choosing the appropriate statistical test for tissue-scoring data to provide the most valid interpretation for the pathologist.