Lecture: Multilevel Regression  

 

Assigned Reading

  • Multilevel regression Video► Read► Slides►

Assignment

Question 1: The following data provides information on long cancer mortality at different medical centers on a large set of patients.  Medical centers claim that they have higher or lower risk for lung cancer mortality because of patients' comorbidities.  Use multi-level analysis to establish if any particular medical center has a statistically significant excess mortality rate beyond what could be expected from patients' comorbidities:

  1. Identify the intercept for each medical center in a regression of 6-month mortality on patients comorbidities
  2. Regress mortality in 6-months on intercepts estimated in previous step.
  3. Report the meaning of these two related regressions 

Use the instructor's last name as the password for the data. Data►

Question 2: In this problem, we approach multi-level modeling through SQL.  In particular, we address question 1 but this time we use SQL instead of regression.  In particular, for each 2 medical centers follow these steps:

  1. Select the data for the medical center.
  2. Use SQL for Level 1 model:
    • In temporary table #Cancer, calculate the probability of mortality for patients with cancer at different combination of comorbidities (called strata).  Call the probability "Probability of Strata + Cancer".  You can accomplish this by selecting the probability where lung cancer =1 and grouping by concatenation of all comorbidities.  To concatenate string variables you can use the + sign.   
    • In temporary table #NoCancer, calculate the probability of mortality for patients without cancer at different strata.  Call the probability "Probability of Strata + No Cancer".
    • Join the tables #Cancer and #NoCancer on Strata. 
  3. Use Regression for Level 2 model:
    • Regress the "Probability of Strata + Cancer" on "Probability of Strata + No Cancer". 
  4. Use the intercept as the impact of cancer in the Medical Center.  

Use the instructor's last name as the password for the data. Data►

Question 3:  Using multi-level modeling, calculate the expected length of stay for the clinician and peer group, after removing the effects of previous Myocardial Infarction (MI) and Congestive Heart Failure (CHF). 

Clinician's Patients

Peer Provider's Patients

Case

Previous MI

CHF

Length of stay

Case

Previous MI

CHF

Length of stay

1

MI

CHF

6

1

MI

CHF

5

2

MI

No CHF

5

2

MI

CHF

5

3

MI

CHF

6

3

No MI

CHF

4

4

MI

CHF

6

4

No MI

No CHF

3

5

MI

CHF

6

5

No MI

CHF

4

6

MI

No CHF

5

6

No MI

CHF

4

7

MI

CHF

6

7

MI

CHF

5

8

MI

No CHF

5

8

MI

CHF

5

9

MI

CHF

6

9

MI

CHF

5

10

MI

No CHF

5

10

MI

CHF

5

11

MI

CHF

6

11

MI

CHF

5

12

No MI

CHF

4

12

No MI

No CHF

3

13

No MI

CHF

4

13

No MI

CHF

4

14

No MI

CHF

4

14

No MI

CHF

4

15

MI

CHF

6

15

No MI

CHF

4

16

MI

CHF

6

16

No MI

CHF

4

17

MI

CHF

6

17

No MI

CHF

4

18

MI

No CHF

5

18

No MI

No CHF

3

19

MI

No CHF

5

19

MI

No CHF

4

20

MI

CHF

6

20

MI

CHF

5

 

 

 

21

MI

CHF

5

 

 

 

22

MI

CHF

5

 

 

 

23

MI

No CHF

4

 

 

 

24

No MI

CHF

4

More

For additional information (not part of the required reading), please see the following links:

  1. A practical guide to multi-level modeling Pubmed►

This page is part of the course on Comparative Effectiveness by Farrokh Alemi PhD Home►  Emailâ–º