Introduction
The main purpose of survival analysis is to understand and calculate the survival rates of cancer patients over time. Researchers can observe participant mortality rates over time without bias thanks to the Kaplan-Meier survival curve, which uses statistics to estimate the number of survivors at any given time (Turkson, 2021). To understand the potential efficacy of treatments in terms of cancer progression, relapse, and mortality rate in a population, researchers in the field of cancer use the Kaplan-Meier analysis.
The Kaplan-Meier analysis will be used to examine the dataset from a clinical breast cancer trial to determine the survival curve for breast cancer patients. A hypothesis test will determine whether the null hypothesis, “the risk that 50% of participants will die from breast cancer within five years,” is accurate or unreliable. This hypothesis will test the mortality rate of the participants over seven years and determine whether there is a chance that half of the participants will develop breast cancer during that time.
Survival Analysis
The goal of survival analysis is to determine how long an event takes and, more specifically, to determine how prognosis, treatment, and other factors affect the survival curves of the population being studied. It considers the fact that if the observation period ends before the event, the time of the event is censored (Liu et al., 2021). Finding factors connected to failure time is the main goal of survival analysis (Ma et al., 2020). Failure in this context can be interpreted as the happening of a specific event, like death or relapse, or as the passing of time.
As it evaluates the average patient survival time, survival analysis is a potent tool in cancer studies. Comparing the expected survival times of various groups or the survival time distributions of various populations is the main goal of survival analysis. It enables investigation into the variables linked to the outcome and their impact on the survival curves.
Kaplan-Meier Analysis
The Kaplan-Meier analysis is used to calculate the mortality rate of a given population and to estimate the likelihood that a patient population will pass away. The Kaplan-Meier estimator is also excellent for assessing long-term effects and determining how treatments affect mortality (Cascini et al., 2020). Estimating the survival rate at a specific time point and comparing the survival rates at various time points are both possible with Kaplan-Meier analysis (Cascini et al., 2020). The cumulative likelihood of survival over time is plotted on the Kaplan-Meier curve, which can be used to infer information about a population’s survival rate. The Kaplan-Meier estimator is primarily used to compare the mortality risks of two or more treatment or exposure groups.
The Kaplan-Meier Curve
The cumulative survival probability over time is plotted on the Kaplan-Meier curve. As it enables the analysis of censored data points, such as the time of death or the occurrence of an event, it is the approach used most frequently for exploratory analyzes of survival data (Siotos et al., 2019). Censored data points were present at the beginning of the trial but were absent by its conclusion. In the Clinical Trial on Breast Cancer dataset, participants will have their survival rates measured in this example using the Kaplan-Meier curve. The null hypothesis, “the risk of 50% of the participants dying from breast cancer will occur within five years,” can be tested using the curve, revealing the participants’ mortality rate over time.
Analysis of the Survival Curve
In the Clinical Trial on the Breast Cancer dataset, the survival curves were determined using the Kaplan-Meier method. A Survival Time chart (Figure 1) depicting the survival rate of the participants over time in intervals of seven years is the result of the analysis. There were 19 participants in the sample at the beginning of the trial, according to the Kaplan-Meier analysis. 11 participants were still alive at the trial’s conclusion, which represents a 58% survival rate.
The participants’ survival rate declined over time on the Kaplan-Meier curve, sharply declining after the fifth year. The trial’s end saw a 58% drop in the survival rate. The analysis’s findings indicate that the null hypothesis is incorrect because there was no risk of 50% of participants dying from breast cancer during the study’s five-year period. According to the Kaplan-Meier analysis, there was a much lower than 50% chance of dying, which was more likely to happen at the end of the seven years.
Conclusion
In conclusion, this essay aimed to estimate the risk that 50% of the participants would pass away from breast cancer within the first five years and to determine the survival curve for breast cancer survivors. The mortality rate of the participants in the Clinical Trial on Breast Cancer dataset was assessed using the Kaplan-Meier method. According to the Survival Time chart plot, the participants’ survival rates declined over time, with a sharp decline occurring after the fifth year. The conclusion of the trial resulted in a 58% decrease in the survival rate. These findings imply that the null hypothesis is incorrect because no participant died of breast cancer at a rate of 50% within five years.
References
Cascini, S., Agabiti, N., Davoli, M., Uccioli, L., Meloni, M., Giurato, L., Marino, C., & Bargagli, A. M. (2020). Survival and factors predicting mortality after major and minor lower-extremity amputations among patients with diabetes: a population-based study using health information systems. BMJ Open Diabetes Research & Care, 8(1), e001355. Web.
Liu, P., Fu, B., Yang, S. X., Deng, L., Zhong, X., & Zheng, H. (2021). Optimizing survival analysis of xgboost for ties to predict disease progression of breast cancer. IEEE Transactions on Biomedical Engineering, 68(1), 148–160. Web.
Ma, W., Zhao, F., Yu, X., Guan, S., Suo, H., Tao, Z., Qiu, Y., Wu, Y., Cao, Y., & Jin, F. (2020). Immune-related lncRNAs as predictors of survival in breast cancer: A prognostic signature. Journal of Translational Medicine, 18(1). Web.
Siotos, C., Naska, A., Bello, R. J., Uzosike, A., Orfanos, P., Euhus, D. M., Manahan, M. A., Cooney, C. M., Lagiou, P., & Rosson, G. D. (2019). Survival and disease recurrence rates among breast cancer patients following mastectomy with or without breast reconstruction. Plastic and Reconstructive Surgery, 144(2), 169e. Web.
Turkson, A. J. (2021). A closer look at the Kaplan-Meier and life table models in survival analysis. OALib, 08(11), 1–19. Web.
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