Cox Proportional Hazard and the effects of weights, statistics homework help

Week 9 assignment

Part one: Cox Proportional Hazard

Dataset

1. Access the dataset in this week’s Learning Resources.
2. Identify the censoring variable, given that you wish to evaluate the event stroke.
3. Identify the time-to-event variable and produce a frequency table to determine the presence of ties.
4. 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?
5. Create a table with the above variables and their role in this analysis.

1. Test the assumption of proportionality

1. Use Kaplan-Meier in SPSS. Create a Hazard plot with time = followed, status=stroke, and factor = hypertension.
2. Interpret the results: Are the baseline hazards proportional and what does this mean in terms of your planned methodology?

1. Use SPSS to run a Cox Proportional Hazards test

1. 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.
2. Create a Hazard Plot for Hypertension (select separate lines)
3. Interpret the results.
4. 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

1. Examine the Data
   1. Open the Week 10 Dataset (SPSS document) located in the Learning Resources area.
   2. Produce numeric descriptive statistics for the following variables:
      • Cregion
      • USR
      • Sex
      • Q1
      • Q6a
      • Q16
      • Q22a
      • Receduc
      • Race/Ethnicity
2. Logistic Regression
   1. 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.
   2. Use backwards stepwise regression to add Receduc to the model as a potential confounder.
   3. 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?
3. Logistic Regression with weights
   1. Weight cases using the variable standwt (Standardized weight)
   2. 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.
   3. How does weighting the cases change the outcome of the logistic regression?
   4. 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.