LQHS2501 - CMLE - Evaluating Effect Size in Histological Data: A Study on Squamous Cell Carcinoma Patients-Evaluating Effect Size in Histological Data: A Study on Squamous Cell Carcinoma Patients

Evaluating Effect Size in Histological Data: A Study on Squamous Cell Carcinoma Patients

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This histology-focused study highlights the importance of effect size measures in laboratory research, particularly when interpreting statistically significant findings. Conducted by Dr. Michal Ordak at the Medical University of Warsaw, the clinical investigation evaluated serum squamous cell carcinoma antigen (SCC Ag) levels across three groups: healthy controls, early-stage (TNM I/II), and advanced-stage (TNM III/IV) squamous cell carcinoma (SCC) patients. Using ANOVA, significant differences in SCC Ag levels were found (p < 0.001), with the highest concentrations in advanced-stage patients.<br /><br />Recognizing p-values alone do not reveal the magnitude or clinical relevance of differences, the diagnostician calculated the eta-squared (η²) effect size, obtaining a very large value of 0.92, indicating 92% of variance in SCC Ag levels was explained by disease stage. Further, grouping patients by clinical reference values (1.5 ng/mL threshold), a chi-square test and Cramér’s V (0.95) confirmed a very strong association between SCC stage and elevated SCC Ag levels. This showcased how effect size complements p-values by quantifying practical significance.<br /><br />The paper stresses that effect size metrics such as eta-squared, omega-squared, Cohen’s d, Hedges’ g, Cramér’s V, and others should be routinely used alongside p-values to provide clearer insights into the real-world impact of findings. This approach enhances diagnostic evaluations, treatment decisions, and research interpretations, preventing misinterpretation caused by sole reliance on p-values that can be influenced by sample size.<br /><br />Quoting statisticians Gene V. Glass and Jacob Cohen, the document underscores that research value lies not just in statistical significance but in measuring the magnitude of effects. Incorporating effect size fosters better communication with clinicians and more reliable patient care.<br /><br />In summary, effect size analysis is critical in histological and clinical laboratory research to complement p-values, quantify clinical importance, and improve evidence-based decision-making in healthcare.

Keywords

effect size

histology

squamous cell carcinoma

SCC antigen

clinical research

ANOVA

eta-squared

Cramér’s V

statistical significance

clinical decision-making