Skučaitė Aldona, Puvačiauskienė Alma, Puišys Rokas, Šiaulys Jonas
Institute of Mathematics, Vilnius University, Naugarduko 24, LT-03225 Vilnius, Lithuania.
Ergo Life Insurance SE, Geležinio Vilko 6A, LT-03150 Vilnius, Lithuania.
Healthcare (Basel). 2021 Apr 1;9(4):383. doi: 10.3390/healthcare9040383.
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changes with time, estimates of survival of cancer patients derived after a long period of observation can become outdated and can be no longer used to estimate survival of patients who were diagnosed later.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 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.
乳腺癌是立陶宛和全球女性因癌症死亡的最常见原因。诊断后的生存几率因诊断时疾病的阶段不同而有显著差异。需要较长的观察期来估计例如15至20年的生存率。此外，由于普通人群的死亡率随时间变化，经过长时间观察得出的癌症患者生存率估计可能会过时，不再适用于估计后来诊断的患者的生存率。因此，构建描述生存概率的分析函数可能会很有用。较短的观察期对于此类构建可能就足够了。我们使用[具体机构]收集的数据进行分析。我们估计了立陶宛乳腺癌患者诊断后长达5年的生存几率。然后我们找到了最适合观察数据的分析生存函数。在本文结尾，我们提供了一些应用示例和进一步研究的方向。我们的研究主要使用了Kaplan-Meier方法。
