Cox Proportional Hazard and the effects of weights, statistics homework help
Week 9 assignment
Part one: Cox Proportional Hazard
Dataset
- Access the dataset in this week’s Learning Resources.
- 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?
Part two: The Effect of Weights
- Examine the Data
- Open the Week 10 Dataset (SPSS document) located in the Learning Resources area.
- Produce numeric descriptive statistics for the following variables:
- Cregion
- USR
- Sex
- Q1
- Q6a
- Q16
- Q22a
- Receduc
- Race/Ethnicity
- Logistic Regression
- Use SPSS to run a logistic regression model with Q22a. “Have you ever looked online for — Information about a specific disease or medical problem?” as your dependent variable (Note that there are 4 levels of responses possible, but only 2 are actually used in the responses so you can state the dependent variable is a binomial and use binary logistic regression) and Sex as the independent variable.
- Use backwards stepwise regression to add Receduc to the model as a potential confounder.
- How does the relationship between Q22a and Sex change with the addition of Receduc? Include a discussion of Odds Ratios and the Model Summary in your answer. Would you consider Receduc a confounder? Is it worth keeping it in the model even if it does not coufound the relationship between Q22a and Sex in this sample?
- Logistic Regression with weights
- Weight cases using the variable standwt (Standardized weight)
- Use SPSS to rerun a logistic regression model with Q22a. “Have you ever looked online for — Information about a specific disease or medical problem?” as your dependent variable, Sex as the independent variable, and Receduc as a covariate.
- How does weighting the cases change the outcome of the logistic regression?
- Discuss why it is important to weight cases from surveys with complex sampling schemes using the differing outcomes you have from parts 2 and 3 of this assignment.