Using Simulation to Model and Validate Invasive Breast Cancer Progression in Women in the Study and Control Groups of the Canadian National Breast Screening Studies I and II

Sharareh Taghipour, Laurent N. Caudrelier, Anthony B. Miller and Bart Harvey

Medical Decision Making, 2017, vol. 37, issue 2, 212-223

Abstract: Background. Modeling breast cancer progression and the effect of various risk is helpful in deciding when a woman should start and end screening, and how often the screening should be undertaken. Methods. We modeled the natural progression of breast cancer using a hidden Markov process, and incorporated the effects of covariates. Patients are women aged 50–59 (older) and 40–49 (younger) years from the Canadian National Breast Screening Studies. We included prevalent cancers, estimated the screening sensitivities and rates of over-diagnosis, and validated the models using simulation. Results. We found that older women have a higher rate of transition from a healthy to preclinical state and other causes of death but a lower rate of transition from preclinical to clinical state. Reciprocally, younger women have a lower rate of transition from a healthy to preclinical state and other causes of death but a higher rate of transition from a preclinical to clinical state. Different risk factors were significant for the age groups. The mean sojourn times for older and younger women were 2.53 and 2.96 years, respectively. In the study group, the sensitivities of the initial physical examination and mammography for older and younger women were 0.87 and 0.81, respectively, and the sensitivity of the subsequent screens were 0.78 and 0.53, respectively. In the control groups, the sensitivities of the initial physical examination for older and younger women were 0.769 and 0.671, respectively, and the sensitivity of the subsequent physical examinations for the control group aged 50–59 years was 0.37. The upper-bounds for over-diagnosis in older and younger women were 25% and 27%, respectively. Conclusions. The present work offers a basis for the better modeling of cancer incidence for a population with the inclusion of prevalent cancers.

Keywords: invasive breast cancer; prevalent cancer; progression modeling; risk factors; over-diagnosis; simulation (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:37:y:2017:i:2:p:212-223

DOI: 10.1177/0272989X16660711

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