Estimation of Sojourn Time in Chronic Disease Screening Without Data on Interval Cases
Article first published online: 25 MAY 2004
Volume 56, Issue 1, pages 167–172, March 2000
How to Cite
Chen, T. H. H., Kuo, H. S., Yen, M. F., Lai, M. S., Tabar, L. and Duffy, S. W. (2000), Estimation of Sojourn Time in Chronic Disease Screening Without Data on Interval Cases. Biometrics, 56: 167–172. doi: 10.1111/j.0006-341X.2000.00167.x
- Issue published online: 25 MAY 2004
- Article first published online: 25 MAY 2004
- Received December 1997. Revised June 1999. Accepted June 1999.
- Breast cancer;
- Chronic disease;
- Interval cases;
- Markov model;
- Sojourn time
Summary. Estimation of the sojourn time on the preclinical detectable period in disease screening or transition rates for the natural history of chronic disease usually rely on interval cases (diagnosed between screens). However, to ascertain such cases might be difficult in developing countries due to incomplete registration systems and difficulties in follow-up. To overcome this problem, we propose three Markov models to estimate parameters without using interval cases. A three-state Markov model, a five-state Markov model related to regional lymph node spread, and a five-state Markov model pertaining to tumor size are applied to data on breast cancer screening in female relatives of breast cancer cases in Taiwan. Results based on a three-state Markov model give mean sojourn time (MST) 1.90 (95% CI: 1.18–4.86) years for this high-risk group. Validation of these models on the basis of data on breast cancer screening in the age groups 50–59 and 60–69 years from the Swedish Two-County Trial shows the estimates from a three-state Markov model that does not use interval cases are very close to those from previous Markov models taking interval cancers into account. For the five-state Markov model, a reparameterized procedure using auxiliary information on clinically detected cancers is performed to estimate relevant parameters. A good fit of internal and external validation demonstrates the feasibility of using these models to estimate parameters that have previously required interval cancers. This method can be applied to other screening data in which there are no data on interval cases.