SAE Institute Clinical Trial Excel Data Analysis Worksheet
Using the Clinical Trial on breast cancer dataset. Perform a Kaplan-Meier Analysis to determine the survival curve for the breast cancer survivors.
H0 The risk of dying from breast cancer will occur within five years. (Null Hypothesis)
H1 The risk of dying from breast cancer does not occur within five years. (Alternative Hypothesis)
Ensure to submit the following requirements for the assignment:
- Review the analysis from the standpoint of how many patients survive over the seven-year time period that the clinical trial covered.
- Present your findings as a Survival Time chart in a Word document, with 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 three scholarly, peer-reviewed articles. One of these sources may be from the class readings, textbook, or lectures, but the others must be external. The Saudi Digital Library is a good place to find these sources and should be your primary resource for conducting research.
- Follow APA 7th edition and Saudi Electronic University writing standards.
Review the grading rubric to see how you will be graded for this assignment.
You are strongly encouraged to submit all assignments to the Turnitin Originality Check prior to submitting them to your instructor for grading.
Saudi Electronic University
College of Health Science
Master of Health Administration
HCM-506: Applied Biostatistics in Health
Introduction
In this paper, I will be discussing the survival rate of breast cancer patients from the time of diagnosis. Cancer has been increasing in modern times due to most people’s lifestyles, exposure to harmful contaminated environments, and even gene inheritance from a person’s lineage that is prone to cause cancers.
two medications and determining subject survival. (Kishore et al., 2010)
Analysis
To build a survival curve, we have to rearrange the data set into a usable format and then use the inbuilt excel chart function to plot the curve. As an initial step, we need to identify all of the distinct values in the time columns. In this case, we use the survival length column after the duplicate values have been extracted from the column. The new time column is copied to a different column and, using the countif function, find whether the patient is dead or alive from the original dataset. This counts the number of patients whose time in the clinical data is more than 0.
The resultant value gives the number of alive patients through each period of time. In the next step, we calculate the reciprocal of the division between the dead and alive patients. The value obtained can now be used to generate a function of time against the start of the patient, either dead or alive. The function of t (time) is then given by the product of the patient’s initial state, which is alive, and the reciprocal value of the division of dead and alive variables.
Fig 1: Sample values
Discussion
Fig 2: survival analysis plot of the breast cancer patients.
From the plot, we can visualize the displacement of the patient data across the time period they are under treatment. For example, we can see that most of the patients have been in the trial for less than 6 years, with most of those patients being in the trial between the periods of 3 to 6 years, inferencing that most of the patients in the trial have been in the trial for a longer time.
The survival curve shows that as the number of years increases that the patient in on the clinical trial, their survival rate increases. These can be seen from the survival analysis plot. As soon as the patient on the clinical trial exceeds 6 years in the trial, their rate of survival increases significantly. Therefore, we can deduce from the analysis that the drug’s effectiveness in the clinical trial can be seen after a patient has used the drug consistently for more than 6 years. (Hung et al., 2018)
Conclusion
References
Chen, Z., Zhang, H., Guo, Y., George, T. J., Prosperi, M., Hogan, W. R., He, Z., Shenkman, E. A., Wang, F., & Bian, J. (2021). Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer’s disease. Npj Digital Medicine, 4(1). https://doi.org/10.1038/S41746-021-00452-1
Hung, M., Bounsanga, J., Voss, M. W., & Saltzman, C. L. (2018). Establishing minimum clinically important difference values for the Patient-Reported Outcomes Measurement Information System Physical Function, hip disability and osteoarthritis outcome score for joint reconstruction, and knee injury and osteoarthritis ou. World Journal of Orthopedics, 9(3), 41–49. https://doi.org/10.5312/wjo.v9.i3.41
Kishore, J., Goel, M., & Khanna, P. (2010). Understanding survival analysis: Kaplan-Meier estimate. International Journal of Ayurveda Research, 1(4), 274. https://doi.org/10.4103/0974-7788.76794
Mr, A., Sharifi J, Mr, P., & Paknahad A. (2015). Breast cancer and associated factors: a review. Journal of Medicine and Life, 8(4), 6–11. https://pubmed.ncbi.nlm.nih.gov/28316699/%0A28316699. SAE Institute Clinical Trial Excel Data Analysis Worksheet
