Bayesian Inference for the Lead Time in Periodic Cancer Screening

This article develops a probability distribution for the lead time in periodic cancer screening examinations. The general aim is to allow statistical inference for a screening program's lead time, the length of time the diagnosis is advanced by screening. The program's lead time is distrib...

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Bibliographic Details
Published in:Biometrics 2007-09, Vol.63 (3), p.873-880
Main Authors: Wu, Dongfeng, Rosner, Gary L., Broemeling, Lyle D.
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Biomedical research
Biometrics
Biometry - methods
Breast cancer
breast neoplasms
Breast Neoplasms - diagnosis
Breast Neoplasms - epidemiology
Breast Neoplasms - prevention & control
Cancer
Cancer screening
Density distributions
Diagnosis, Computer-Assisted - methods
Disease models
Early detection
Female
Humans
Inference
Lead time
Mass Screening - methods
Medical screening
Mortality
Musical intervals
Patient assessment
Periodic screening exams
Population Surveillance - methods
Reproducibility of Results
Research methodology
Risk Assessment - methods
Risk Factors
Sensitivity
Sensitivity and Specificity
Simulation
Sojourn time
Statistical analysis
Time Factors
Transition probability
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Summary:This article develops a probability distribution for the lead time in periodic cancer screening examinations. The general aim is to allow statistical inference for a screening program's lead time, the length of time the diagnosis is advanced by screening. The program's lead time is distributed as a mixture of a point mass and a piecewise continuous distribution. Simulation studies using the HIP (Health Insurance Plan for Greater New York) study's data provide estimates of different characteristics of a screening program under different screening frequencies. The components of this mixture represent two aspects of screening's benefit, namely, a reduction in the number of interval cases and the extent by which screening advanced the age of diagnosis. We present estimates of these two measures for participants in a breast cancer screening program. We also provide the mean, mode, variance, and density curve of the program's lead time. The model can provide policy makers with important information regarding the screening period, frequency, and the endpoints that may serve as surrogates for the benefit to women who take part in a periodic screening program. Though the study focuses on breast cancer screening, it is also applicable to other kinds of chronic disease.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.1541-0420.2006.00732.x