Abstract
Breast cancer is the most common cause of mortality due to cancer for women both in Lithuania and worldwide. Chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis. Extended term periods are required to estimate survival of, e.g., 15–20 years. Moreover, since mortality of the average population changes with time, estimates of survival of cancer patients derived after a long period of observation can become outdated and can be no longer used to estimate survival of patients who were diagnosed later. Therefore, it can be useful to construct analytic functions that describe survival probabilities. Shorter periods of observation can be enough for such construction. We used the data collected by the Lithuanian Cancer Registry for our analysis. We estimated the chances of survival for up to 5 years after patients were diagnosed with breast cancer in Lithuania. Then we found analytic survival functions which best fit the observed data. At the end of this paper, we provided some examples for applications and directions for further research. We used mainly the Kaplan–Meier method for our study.
Highlights
Breast cancer is the most common cause of mortality due to cancer for women both in Lithuania and worldwide in the recent years
Cardiovascular disease is more threatening for women aged 60 and above when mortality rates due to this reason increase from 30% in the age group of 60–64 to almost 80% in the oldest age group (85+)
After the construction of estimates of survival functions and their projections, it became clear that steps taken to diagnose breast cancer during Stage 1 of disease are psychologically, and financially sensible
Summary
Breast cancer is the most common cause of mortality due to cancer for women both in Lithuania and worldwide in the recent years. Though there were some deaths due to cancer or cardiovascular diseases among girls and women of young age (10–34), mortality rates due to both causes increased quite significantly with age Both reasons, result in a slightly different mortality pattern. Narod et al [7] used the Kaplan–Meier technique, and time to death histograms to estimate mortality of women who died of breast cancer during the 20 year period after diagnosis. Even after estimating survival for longer periods, it will be unclear whether results may be used to predict mortality of patients diagnosed during recent years. We analyzed mortality of breast cancer patients in Lithuania using two different approaches: ratio of deaths to exposure and the Kaplan–Meier estimator. The possible applications of our research are discussed in the concluding Section 5
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
