Inference of Long-Term Screening Outcomes for Individuals with Screening Histories. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Wu, Dongfeng; Kafadar, Karen; Rai, Shesh N.
• Is Part Of: Statistics and public policy. Volume 5:Issue 1(2018)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-01-01
• Subjects: No-early-detection -- Overdiagnosis -- Sensitivity -- Sojourn time -- Symptom-free-life -- True-early-detection; Policy sciences -- Methodology -- Periodicals; Social sciences -- Statistical methods -- Periodicals; Medical statistics -- Methodology -- Periodicals; Statistics -- Periodicals; Medical statistics -- Methodology; Policy sciences -- Methodology; Social sciences -- Statistical methods; Statistics; Periodicals; 320.60727
• DOI: 10.1080/2330443X.2018.1438939
• Languages: English
• ISSNs: 2330-443X

Abstract

ABSTRACT: We develop a probability model for evaluating long-term outcomes due to regular screening that incorporates the effects of prior screening examinations. Previous models assume that individuals have no prior screening examinations at their current ages. Due to current widespread medical emphasis on screening, the consideration of screening histories is essential, particularly in assessing the benefit of future screening examinations given a certain number of previous negative screens. Screening participants are categorized into four mutually exclusive groups: symptom-free-life, no-early-detection, true-early-detection, and overdiagnosis . For each case, we develop models that incorporate a person's current age, screening history, expected future screening frequency, screening test sensitivity, and other factors, and derive the probabilities of occurrence for the four groups. The probability of overdiagnosis among screen-detected cases is derived and estimated. The model applies to screening for any disease or condition; for concreteness, we focus on female breast cancer and use data from the study conducted by the Health Insurance Plan of Greater New York (HIP) to estimate these probabilities and corresponding credible intervals. The model can provide policy makers with important information regarding ranges of expected lives saved and percentages of true-early-detection and overdiagnosis among the screen-detected cases.