Cox Proportional Hazard Survival Analysis to Inpatient Breast Cancer Cases

Bustan, Muhammad Nadjib; Arman, Arman; Aidid, Muhammad Kasim; Gobel, Fatmah Afrianty; Syamsidar,

doi:10.1088/1742-6596/1028/1/012230

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…Better survival is more likely due to the earlier stage of the disease rather than participation in CAPs or the later period of diagnosis. Our results are in line with those obtained in [17], which states the following: The main factor causing low survival time was because the patient comes for treatment already in an advanced stage even accompanied by comorbidities (such as diabetes, anemia and hypertension).…”

Section: Discussionsupporting

confidence: 92%

“…This method is widely used to model survival after diagnosis depending on various risk factors (hazards). For instance, in [14][15][16][17], the Cox proportionate hazards method was used for a survival analysis. An interesting approach was given by Putter et al [18], who proposed a method to deal with time-dependent variables when using the Cox model for survival analysis among lung cancer patients.…”

Section: Introductionmentioning

confidence: 99%

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Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females

Levickytė,

Skučaitė,

Šiaulys

et al. 2024

Healthcare

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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.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females

Levickytė,

Skučaitė,

Šiaulys

et al. 2024

Healthcare

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show abstract

“…Moreover, in a retrospective cohort study of in-patient breast cancer cases in Indonesia ( 8 ), important factors associated with survival rate were found to be metastasis and comorbidity.…”

Section: Discussionmentioning

confidence: 99%

“…When the diagnosis is made early, successful treatment results can be obtained and the mortality rate can be reduced. For this reason, experts in all regions of the world are working on breast cancer ( 2 – 8 ). Because knowing every information and the factors that may affect the treatment processes positively or negatively regarding the course of breast cancer can help develop strategies for both improving the quality of life of patients and reducing the cost of treatment.…”

Section: Introductionmentioning

confidence: 99%

Survival Rate and Prognostic Factors in Turkish Women Patients with Breast Cancer

Zorlutuna¹

2022

ijph

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Background: The study aimed to estimate the overall and disease-free survival rates of breast cancer patients and the factors affecting these rates. Methods: In this retrospective study, data were obtained from 686 patients diagnosed with breast cancer in Sivas Cumhuriyet University Faculty of Medicine Research and Application Hospital Oncology Center between 1988 and 2014. Total population sampling method was used. The survival rates at certain periods were determined by creating a Life Table. By using the Kaplan-Meier Analysis, the mean survival times and rates were determined, and whether the variables had an impact on survival was examined. By applying Cox regression analysis, the effect of prognostic factors that are significant on the survival time of breast cancer patients was examined. Results: Overall mean survival time was found as 208.4±11.8 months. According to Kaplan-Meier analysis, 1, 5, 10 and 20-years overall survival rates were 96.6 ± 0.07%, 82.3 ± 1.7%, 64.4 ± 3.4% and 49%± 7.4%, respectively. According to Cox regression analysis results, variables that influence overall survival time were found as disease stage, multicentricity status, ECOG (performance status), presence of diabetes, CA15-3 value, neutrophil/lymphocyte ratio. Moreover, variables that had an impact on the disease-free survival time were found as tumor grade, multicentricity, and ECOG. Conclusion: Many factors other than disease can prolong survival or accelerate death. Considering the findings of this study may be useful in planning the treatment of breast cancer patients have positive affect on overall survival rates.

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“…This method is widely used to model survival after diagnosis depending on various risk factors (hazards). For instance in [15][16][17][18], the Cox proportionate hazards method is used for survival analysis. An interesting approach is given by Putter et al [19], where the authors suggest the way how to deal with time dependent variables when using the Cox model for survival analysis among lung cancer patients.…”

Section: Of 13mentioning

confidence: 99%

Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females

Levickytė,

Skucaitė,

Šiaulys

et al. 2024

Preprint

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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).

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Cox Proportional Hazard Survival Analysis to Inpatient Breast Cancer Cases

Cited by 7 publications

References 26 publications

Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females

Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females

Survival Rate and Prognostic Factors in Turkish Women Patients with Breast Cancer

Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females

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