Due 4/28/19 7 p.m EST
Be on time, Original Work, Know SPSS, READ Instructions before asking for work!!
Data Attached along with Step by Step Guide.
Cox Proportional Hazard is a regression technique that incorporates the element of time-to-event into the computation of a hazard ratio. A censoring variable is one for which information is only partially known. A time-to-event variable is one that encapsulates the time between starting a treatment and onset of a new event.
For this Assignment, you identify a censoring variable and a time-to-event variable. You also identify an independent variable that represents exposure status (with exposure or without exposure). You then conduct Cox Proportional Hazard regression analysis using these variables to compare survival times for those with and without the exposure. Finally, you interpret the output of the regression, including the testing of the assumption of proportionality and the hazards ratio. Use the Week 9 Dataset (SPSS Document)
Access the dataset attached
Identify the censoring variable, given that you wish to evaluate the event stroke.
Identify the time-to-event variable and produce a frequency table to determine the presence of ties
Identify the independent variable for exposure status, given the following research question: Is there an association between hypertension and the time a person was followed before experiencing a stroke?
Create a table with the above variables and their role in this analysis.
Test the assumption of proportionality
Use Kaplan-Meier in SPSS. Create a Hazard plot with time = followed, status=stroke, and factor = hypertension.
Interpret the results: Are the baseline hazards proportional and what does this mean in terms of your planned methodology?
Use SPSS to run a Cox Proportional Hazards test
Use stroke as the event and hypertension as the factor. (be sure to identify hypertension as a categorical variable). Be sure to include the output in your submission.
Create a Hazard Plot for Hypertension (select separate lines)
Interpret the results.
Could the presence of ties in time followed (from the frequency table in 1c) affect the results? How might you accommodate ties in this analysis?
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