I am in the hospital with my wife who is having complications from a cancer treatment. I am looking for someone to help me complete the following R-Studio work. I would perter to have completed by 4/24 but will consider time frames suggested.
These problems use the Copelan bone marrow transplant dataset, available in the KMsurv package.
1. Install and load KMsurv, then load the ‘bmt’ dataset. Create factors if appropriate for further analysis. You can find variable definitions with the command
2. Generate appropriate plots and summary statistics to explore the dataset and check for problem data.
3. Perform logistic regression to model one year post-transplant survival. Are there observations without full follow up? Explain your strategy for dealing with any missing outcome data. In your report, include the code used and the output from your final model. For one of the significant variables, explain what the odds ratio is and how to interpret it.
4. Plot disease free survival time using Kaplan-Meier. Plot the curves and comment on what you see. What is the three year disease free survival of the three patient types? Check whether these curves are different using the log-rank test.
5. Build a Cox model for post-transplant survival. Manually build the best model, explaining why you included and excluded each variable. Compare your results to the logistic regression model. Which do you think is the better approach, and why?
6. Load the library ‘MASS’ and the dataset ‘birthwt’. For this section, assume that there is no missing or bad data, no data transformations are required, and that the assumptions of each test are met.
Answer the following questions. For each question, write a statement of the null and alternative hypothesis, include the code you used for the statistical test (this should not be more than 1-3 lines), the R output from the statistical test, and whether the null hypothesis was rejected. You will graded based on inclusion of all required elements, appropriate choice of statistical test, and correct implementation and interpretation of the test.
7. Is there a relationship between race and smoking status?
8. Is there a relationship between mother’s age and mother’s weight?
9. Is there a relationship between number of previous premature labors and low birthweight?
10. Create a multivariate model predicting birth weight
A. What are the significant predicators?
B. Explain why this is not a very clinically useful model
11. Create a multivariate model predicting if a child will have low birth weight
A. What are the significant predictors?
B. What is the increase in the odds of having a low birthweight baby if the mother smokes?