Using The Survival Curve Dataset Tab In Framingham

Using The Survival Curve Dataset Tab Located In The Framingham Heart S

Using the Survival Curve dataset tab located in the Framingham Heart Study Dataset, perform a Kaplan-Meier test to determine the survival curve for the Survival Curve data. Upload the Excel spreadsheet into R Studio or perform the Kaplan-Meier test in Excel. H0 The survival time is not related to the patient treatment group. (Null Hypothesis) H1 The survival time is related to the patient treatment group. (Alternative Hypothesis) Present your findings as a Survival Curve chart in a Word document, including a title page, introduction explaining why you would conduct a survival analysis, a discussion where you interpret the meaning of the survival analysis, and a conclusion. Your submission should be 3-4 pages to discuss and display your findings. Provide support for your statements with in-text citations from a minimum of four scholarly, peer-reviewed articles. Two of these sources may be from the class readings, textbook, or lectures, but the others must be external. Follow APA a

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Using The Survival Curve Dataset Tab Located In The Framingham Heart S

Survival analysis is a statistical approach used to examine the time until an event occurs, such as death, relapse, or failure of a treatment. It is particularly useful in medical research for understanding patient prognosis and treatment effectiveness over time. The Kaplan-Meier estimator, a non-parametric statistic, is widely used in survival analysis to estimate the survival function from life-time data, especially when data are censored. In this context, the survival analysis aims to evaluate whether the treatment group influences survival times among patients, which can directly inform clinical decisions and healthcare policies.

The dataset in question, derived from the Framingham Heart Study, provides an ideal basis for conducting a Kaplan-Meier analysis. This dataset includes patient survival times, treatment groups, and censoring information. The primary goal is to test the null hypothesis (H0) that there is no difference in survival times between treatment groups against the alternative hypothesis (H1) that a difference exists.

Performing the Kaplan-Meier analysis involves first preparing the dataset in R or Excel, then generating survival curves for the different treatment groups. Using R software is recommended because it offers robust packages for survival analysis, such as 'survival' and 'survminer'. The analysis begins with importing the dataset into R, followed by creating a survival object that encapsulates survival time, censoring status, and treatment group. The Kaplan-Meier estimator is applied to this object, and survival curves are visualized to display the probability of survival over time for each treatment group. The log-rank test assesses whether the survival differences between groups are statistically significant, thereby testing the stated hypotheses.

The resulting survival curve provides visual and statistical insights into the effect of treatment on patient survivability. If the survival curves differ significantly, the null hypothesis would be rejected, indicating that treatment impacts survival. Conversely, overlapping curves suggest no effect, supporting the null hypothesis. These findings are critical for clinicians evaluating treatment efficacy and for researchers designing future studies.

In the discussion, interpretation of the survival curves involves analyzing the shape, separation, and statistical test results. A significant divergence between curves suggests that treatment groups experience different survival probabilities, which could be attributed to treatment efficacy or other confounding factors. Conversely, overlapping curves imply comparable survival regardless of treatment, possibly indicating minimal treatment effect or insufficient sample size.

In conclusion, survival analysis via Kaplan-Meier provides valuable insights into the impact of treatments on patient survival times. By applying this method to the Framingham dataset, researchers can objectively evaluate the relationships between treatment groups and survival outcomes. Ultimately, these insights contribute to improving clinical decision-making, personalized medicine, and health outcomes.

References

• Klein, J. P., & Moeschberger, M. L. (2003). Survival analysis: Techniques for censored and truncated data. Springer Science & Business Media.
• Kirkwood, B. R., & Sterne, J. A. C. (2003). Essential Medical Statistics (2nd ed.). Blackwell Publishing.
• Therneau, T. (2020). A Package for Survival Analysis in R. https://cran.r-project.org/web/packages/survival/index.html
• Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6923), 1552.
• Hosmer, D. W., Lemeshow, S., & May, S. (2008). Applied Survival Analysis: Regression Modeling of Time-to-Event Data. Wiley.