Order Description
Cox Proportional Hazards
While the Kaplan-Meier Curve allows you to infer a relationship between a single exposure and survival times, it is a univariate test, so cannot take into account the effects of confounders on the relationship of exposure to time-to-event.
Cox Proportional Hazards (CPH) is a form of regression analysis that compares hazard rates across multiple exposures or risks. It uses a censoring variable and accounts for time-to-event in order to evaluate longitudinal data. The output of Cox Proportional Hazards in SPSS and other statistical software is known as the hazard ratio.
The hazard ratio provides information about risk, for example:
A group of oncologists is considering a treatment for one of their cancer patients. They review a study that tested a new medication for the same type and stage of cancer that this patient has. Among the results, they find that patients were twice as likely to experience complications while taking the treatment as those who did not use it. This finding is represented as a hazard ratio of 2 associated with the use of this treatment, and it gives the oncologists critical information as they consider treatment options for their patient.
This week, you examine Cox Proportional Hazards and its application in public health research.
Students will:
• Analyze the use of proportional hazards in research
• Compare the use of logistic regression and proportional hazards
• Analyze censor variables and time-to-event
• Apply methods to perform Cox Proportional Hazards test
• Interpret results of Cox Proportional Hazards test
For this Assignment, you review the media titled, “Cox Proportional Hazards” in this week’s Learning Resources. The media highlights Dr. Diane Neal’s research, which used CPH to compare risk of fetal death during ante- and intrapartum periods based on prenatal care exposure. You compare CPH to logistic regression.
With these thoughts in mind:
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 Cox Proportional Hazards used?
• What was the main result(s)?
• What was the interpretation?
• What are your thoughts on the limitation(s) of the study?
For the Assignment
Cohen, H. W., Hailpern, S. M., & Alderman, M. H. (2008). Sodium intake and mortality follow-up in the third National Health and Nutrition Examination Survey (NHANES III). Journal of General Internal Medicine, 23(9), 1297–1302.
Note: Retrieved from Walden Library databases.
Kwok, J., Langevin, S. M., Argiris, A., Grandis, J. R., Gooding, W. E., & Taioli, E. (2010). The impact of health insurance status on the survival of patients with head and neck cancer. Cancer, 116(2), 476–485.
Note: Retrieved from Walden Library databases.
Marshall, N. S., Wong, K. K. H., Liu, P. Y., Cullen, S. R. J., Knuiman, M. W., & Grunstein, R. R. (2008). Sleep apnea as an independent risk factor for all-cause mortality: The Busselton Health Study. Sleep, 31(8), 1079–1085.
Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2542953/.
Khaw, K.-T., Wareham, N., Bingham, S., Welch, A., Luben, R., & Day, N. (2008). Combined impact of health behaviours and mortality in men and women: The EPIC-Norfolk Prospective Population Study. PLOS Medicine, 5(1), e12.
Note: Retrieved from Walden Library databases.
