Survival and Longitudinal Analysis
Survival analysis enables the examination of the probability of survival.
Insurers use survival analysis to set premiums for their customers based on the conditional probability that a 65-year-old has an additional 10 years of life. Public health researchers might use survival analysis to review survival times following various cancer treatments. This may enable medical professionals to estimate possible survival rates their patients may experience. If a patient has cancer, medical professionals can use the results of survival analysis to determine that patient’s likelihood of survival for more than 5 years according to disease characteristics.
Longitudinal analysis evaluates data collected over a period of time (longitudinal studies). Well-known examples of longitudinal studies in public health include the Framingham Heart Study, the Nurses’ Health Study, the Birmingham Study, and the CDC’s Longitudinal Studies on Aging.
This week, you conduct survival and longitudinal analysis using the Kaplan-Meier method. You also analyze research studies using the same method. In addition, you continue to improve your statistical skills by estimating survival distributions and determining whether or not they are significant.
Students will:
• Evaluate use of Kaplan-Meier
• Analyze research study conclusions derived from use of Kaplan-Meier method
• Evaluate use of survival analysis method
• Create a Kaplan-Meier curve
• Interpret a Kaplan-Meier curve
• Estimate survival distributions
• Evaluate whether survival distributions are significant
Survival Analysis
Reflect on the introduction about survival analysis. If you recall, survival analysis examines probability of survival. This is a useful type of longitudinal analysis. Longitudinal analysis evaluates data collected over a period of time through longitudinal studies. Survival analysis techniques allow for the inclusion of time until an event occurs as an essential variable in the relationship of risk and outcome. In public health, use of survival analysis is critical to the study of risks, interventions, treatments, and outcomes.
For this Assignment, you review the articles in the references listed below.
With these thoughts in mind:
Select one article in the References listed below foe the assignment
Post find and discuss the following key elements of the article you selected:
• Identify variables: independent variable(s), dependent variable(s), and confounders.
• What was the research question?
• Why was Kaplan-Meier used?
• What was the main result(s)?
• What was the interpretation?
• What are your thoughts on the limitation(s) of the study?
See reading material below
Daniel, WW & Cross, CL. (2013). Biostatistics: A Foundation for Analysis in the Health Sciences. Hoboken, NJ: Wiley.
• Chapter 14: Survival Analysis (pp. 750-767)
Telke, S. E., & Eberly, L. E. (2011). Statistical hypothesis testing: Associating patient characteristics with an incident condition: Kaplan-Meier curves, hazard ratios, and Cox proportional hazards regression. Journal of Wound, Ostomy, and Continence Nursing, 38(6), 621–626.
Note: Retrieved from Walden Library databases.
For the Assignment.
Hoeper, M. M., Markevych, I., Spiekerkoetter, E., Welte, T., & Niedermeyer, J. (2005). Goal-oriented treatment and combination therapy for pulmonary arterial hypertension. European Respiratory Journal, 26(5), 858-863.
Note: You will access this article from the Walden Library databases.
Sterling, T., Vlahov, D., Astemborski, J., Hoover, D., Margolick J. & Quinn, T. (2001). Initial plasma HIV-1 RNA levels and progression to AIDS in women and men. New England Journal of Medicine, 344, 720-725.
Note: Retrieved from Walden Library databases.
Hanaoka, T., Mita, K., Hiramoto, A, Suzuki, Y., Maruyama, S., Nakadate, T., Kishi, R., Okado, K. & Egusa, Y. (2010). Survival prognosis in Japanese with severe motor and intellectual disabilities living in public and private institutions between 1961 and 2003. Journal Epidemiology, 20(1). 77-81.
Note: Retrieved from Walden Library databases.
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