Version 1
: Received: 8 January 2024 / Approved: 8 January 2024 / Online: 9 January 2024 (10:29:11 CET)
How to cite:
Levickytė, J.; Skucaitė, A.; Šiaulys, J.; Vincerževskienė, I. Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females. Preprints2024, 2024010654. https://doi.org/10.20944/preprints202401.0654.v1
Levickytė, J.; Skucaitė, A.; Šiaulys, J.; Vincerževskienė, I. Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females. Preprints 2024, 2024010654. https://doi.org/10.20944/preprints202401.0654.v1
Levickytė, J.; Skucaitė, A.; Šiaulys, J.; Vincerževskienė, I. Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females. Preprints2024, 2024010654. https://doi.org/10.20944/preprints202401.0654.v1
APA Style
Levickytė, J., Skucaitė, A., Šiaulys, J., & Vincerževskienė, I. (2024). Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females. Preprints. https://doi.org/10.20944/preprints202401.0654.v1
Chicago/Turabian Style
Levickytė, J., Jonas Šiaulys and Ieva Vincerževskienė. 2024 "Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females" Preprints. https://doi.org/10.20944/preprints202401.0654.v1
Abstract
Breast cancer is the most common cause of mortality due to cancer for woman both in Lithuania and worldwide. 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 case of countries with lower numbers of inhabitants) dividing data into subsets, say, by stage at diagnosis may lead to results where some subsets contain too little data so that results of Kaplan Meier (or any other) method will become statistically incredible. The problem may become even more acute if the researcher would like to use more risk factors, such as stage at diagnosis, sex, place of living, treatment method, etc. Alternatively, Cox models are used to analyse survival data with covariates, and they don’t require dividing the data into subsets according to chosen risks factors (hazards).
Keywords
breast cancer; central death rate; exposure to risk; Kaplan – Meier estimate; survival analysis; stratified Cox model; cancer awareness campaign
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
