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 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.
Wu
Dongfeng
D
Department of Mathematics and Statistics, Mississippi State University, Mississippi State, Mississippi 39762, USA. dwu@math.msstate.edu
Rosner
Gary L
GL
Broemeling
Lyle D
LD
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology* / Q000517:prevention & control; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005260:Female; D006801:Humans; D008403:Mass Screening / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013997:Time Factors