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Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females.
- Source :
- Healthcare (2227-9032); Apr2024, Vol. 12 Issue 7, p746, 20p
- Publication Year :
- 2024
-
Abstract
- Breast cancer is the most common cause of mortality due to cancer for women both in Lithuania and worldwide. The chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis and other factors. One way to estimate survival is to construct a Kaplan–Meier estimate for each factor value separately. However, in cases when it is impossible to observe a large number of patients (for example, in the case of countries with lower numbers of inhabitants), dividing the data into subsets, say, by stage at diagnosis, may lead to results where some subsets contain too few data, thus causing the results of a Kaplan–Meier (or any other) method to become statistically incredible. The problem may become even more acute if researchers want to use more risk factors, such as stage at diagnosis, sex, place of living, treatment method, etc. Alternatively, Cox models can be used to analyse survival data with covariates, and they do not require the data to be divided into subsets according to chosen risks factors (hazards). We estimate the chances of survival for up to 5 years after a breast cancer diagnosis for Lithuanian females during the period of 1995–2016. Firstly, we construct Kaplan-Meier estimates for each stage separately; then, we apply a (stratified) Cox model using stage, circumstance of diagnosis, and year of diagnosis as (potential) hazards. Some directions of further research are provided in the last section of the paper. [ABSTRACT FROM AUTHOR]
- Subjects :
- BREAST cancer prognosis
BREAST tumor diagnosis
HEALTH literacy
SURVIVAL rate
BREAST tumors
EARLY detection of cancer
DESCRIPTIVE statistics
LITHUANIANS
REPORTING of diseases
CHI-squared test
KAPLAN-Meier estimator
LOG-rank test
TUMOR classification
SURVIVAL analysis (Biometry)
DATA analysis software
PROPORTIONAL hazards models
Subjects
Details
- Language :
- English
- ISSN :
- 22279032
- Volume :
- 12
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Healthcare (2227-9032)
- Publication Type :
- Academic Journal
- Accession number :
- 176593648
- Full Text :
- https://doi.org/10.3390/healthcare12070746