This study presents a computer model designed to estimate the effect on life expectancy of HRT in women, according to their risks for coronary disease and breast cancer. In order to model the disease processes being studied here, the authors used a Markov state transition model. Markov analysis is a way of analyzing complex systems. The model considers two initially healthy cohorts of 50 year-old women, one cohort taking HRT and one cohort not taking it. The model assumes that, each year, women are at risk for developing breast cancer, coronary disease and hip fractures.
Women are also at risk for dying from other causes.
It is assumed that the effect of HRT is primarily on the likelihood of developing breast cancer, CAD or an osteoporotic hip fracture, not on the likelihood of dying from it once such an event has occurred. In order to investigate the effect of a priori risk on outcomes, the model is run in cohorts of women with different risk factor profiles. CAD was defined as an MI, the development of angina or sudden cardiac death. This allows an individual to be assigned a specific numeric level of risk, according to the combination of risk factors. A risk weight was calculated analogous to that for CAD, allowing a woman to be classified according to the degree of risk for breast cancer.