Work

Statistically Significant Association Does not Imply Improvement in Prediction of Clinical Outcomes

Year: 2025

Type: article

Abstract: In the current landscape of clinical studies, the concept of statistically significant association is often mixed up with the expectation of improved prediction performance. We discuss the two concepts, association and prediction, and present the epidemiologic principles and statistical constructs that underlie the discrepancy between statistically significant associations and the rationale for their lack of impact on improving prediction in terms of discrimination. This issue is illustrated using an existing breast cancer dataset. The concept of statistically significant association should not be mixed up with the expectation of improved discrimination performance. Although some markers may not markedly improve discrimination, they can still have substantial clinical relevance by identifying critical biological pathways that inform novel treatment or prevention strategies. Development of models for both association and prediction assessments should be directly tied to clinical translation to move adoption forward to advance precision medicine. PREVENTION RELEVANCE: Development of models for both association and prediction assessments should be directly tied to clinical translation to move adoption forward to advance precision medicine.

Source: Cancer Prevention Research

DOI: 10.1158/1940-6207.capr-25-0056

Authors: Shu Jiang, Bernard A. Rosner, Graham A. Colditz

Institutions: Washington University in St. Louis, Brigham and Women's Hospital

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: Breast Cancer Research Foundation, Washington University School of Medicine in St. Louis, National Cancer Institute