Modelling the cumulative risk for a false-positive under repeated screening events.

Area of Science:

• Medical screening
• Biostatistics
• Epidemiology

Background:

• Screening examinations are crucial for early disease detection, enabling timely treatment.
• However, screening procedures like mammograms can lead to false-positive and false-negative diagnoses.
• The cumulative risk of false-positive results increases with the number of screening events.

Purpose of the Study:

• To quantify the cumulative risk of false-positive diagnoses associated with repeated screening examinations.
• To develop a general framework for investigating this risk at both population and individual levels.
• To enable individualized risk assessment by incorporating evolving patient medical history.

Main Methods:

• Modeling cumulative risk based on the number of screening events until the first false-positive.
• Utilizing actuarial models adapted for life table data.
• Incorporating Cox regression for individual-level modeling.
• Employing a Bayesian inference framework for analysis.

Main Results:

• The study quantifies the cumulative risk of false-positive results from repeated screenings.
• A framework is established for both population-level and individualized risk assessment.
• The models were applied to a dataset of 9773 screening mammograms from 2227 women.

Conclusions:

• Repeated screening increases the cumulative risk of false-positive diagnoses.
• The developed framework allows for personalized risk assessment, considering individual medical history.
• This approach can help optimize screening strategies and reduce unnecessary follow-up procedures.