Inference of Onset Age of Preclinical State and Sojourn Time for Breast Cancer

Main Article Content

Donfeng Wu Seongho Kim

Abstract

Aims: Accurate estimation of the three key parameters (sensitivity, time duration in disease-free state and sojourn time in preclinical state) in cancer screening are critical. Likelihood method with a new link function was applied to the Health Insurance Plan of Greater New York (HIP) breast cancer screening data, to estimate the onset age of preclinical state and the sojourn time in the preclinical state for breast cancer.


Materials and Methods: A new link function to model sensitivity as a function of time in the preclinical state and the sojourn time was adopted. Markov Chain Monte Carlo simulations were used to obtain posterior samples and make inference on the three key parameters. Maximum likelihood estimate was also used for comparison.


Results: The onset age of the preclinical state has a wide range for breast cancer; the peak onset age was 65.07 years (95% credible interval [C.I.], 55.76 to 73.02). The mean sojourn time was 2.00 years (95% C.I., 0.85 to 2.95). The 95 % C.I. for the sojourn time was 0.16 to 5.53 years. Sensitivity at onset of the preclinical state was 0.75 (95% C.I., 0.54 to 0.88); and sensitivity at the end of the preclinical state was 0.84 (95% C.I., 0.67 to 0.88).   


Conclusion: The HIP study was the oldest breast cancer mass screening. The estimates reflect key parameters in those days with lower screening sensitivity. However, it is helpful to know other parameters in the planning for future breast cancer screening.

Article Details

How to Cite
WU, Donfeng; KIM, Seongho. Inference of Onset Age of Preclinical State and Sojourn Time for Breast Cancer. Medical Research Archives, [S.l.], v. 10, n. 2, feb. 2022. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/2665>. Date accessed: 06 dec. 2022. doi: https://doi.org/10.18103/mra.v10i2.2665.
Section
Research Articles

References

[1] https://www.breastcancer.org/symptoms/understand_bc/statistics. Accessed 10/19/2021.
[2] https://www.cancer.net/cancer-types/breast-cancer/statistics. Accessed 10/19/2021.
[3] https://www.cancer.net/cancer-types/breast-cancer-metastatic/statistics. Accessed 11/2/2021.
[4]https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/breast-cancer-screening. Accessed 11/20/2021.
[5] https://www.cdc.gov/nchs/data/vsrr/VSRR10-508.pdf. Accessed 11/20/2021.
[6] Wu D, Rosner GL, Broemeling L. (2005). MLE and Bayesian inference of age-dependent sensitivity and transition probability in periodic screening. Biometrics. 2005; 61(4): 1056-1063.
[7] Wu D, Rai SN, and Seow A. (2021). Estimation of preclinical state onset age and sojourn time for heavy smokers in lung cancer. Statistics and Its Interface. Accepted. NIHMS ID: 1734205.
[8] Walter SD, Day NE. Estimation of the duration of a pre-clinical disease state using screening data. Am J Epidemiol. 1983; 118(6): 865-886.
[9] Joe, BN. And Sickles, EA. (2014). The evolution of breast imaging: past to present. Radiology. 273 (2). S23-S44.
[10] Chen Y, Brock GN and Wu D. (2010). Estimating key parameters in periodic breast cancer screening - application to the Canadian National Breast Screening Study data. Cancer Epidemiology. 34, 429-433. DOI: 10.1016/j.canep.2010.04.001.
[11] Kim S, and Wu D. (2016). Estimation of sensitivity depending on sojourn time and time spent in preclinical state. Statistical Methods in Medical Research. 2016, Vol. 25(2), 728-740.
DOI: 10.1177/0962280212465499.
[12] Wu D, Kim S (2020). Problems in the estimation of the key parameters using MLE in lung cancer screening. J Clin Res Rep. 2020; 5(3).