Work

Implications of outcome misclassification in risk effect modeling in cancer population studies

Year: 2026

Type: article

Abstract: Accurate cancer outcome ascertainment is crucial for estimating cancer risk in cohort and case-control studies. Common methods, such as self-reports and cancer registries, can be prone to false positives and false negatives. This study evaluates the performance of cancer risk models subject to imperfect cancer outcome ascertainment.

Source: Annals of Cancer Epidemiology

DOI: 10.21037/ace-2025-3

Authors: Liana A. Hill, Paul S. Albert, Jonine D. Figueroa, Danping Liu

Institutions: Division of Cancer Epidemiology and Genetics, University of Maryland, Baltimore County, National Institutes of Health, National Cancer Institute

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: diamond

OpenAlex ID: W7159638848

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