20 - Stanford University



Lab Seven: Conditional logistic regression for matched data and ordinal logistic regression for ordinal response variables

Lab Objectives

After today’s lab you should be able to:

1. Run conditional logistic regression for matched data.

2. Interpret output from conditional logistic regression.

3. Run an ordinal logistic regression

4. Interpret output from ordinal logistic regression.

LAB EXERCISE STEPS:

Follow along with the computer in front…

1. Download the TWO lab 7 datasets (matched and runners) from the class website (already in SAS format!).

stanford.edu/~kcobb/courses/hrp261 (right click on LAB 7 DATA (matched, runners)(save to desktop

Dataset 1: matched:

Researchers in a Midwestern county tracked flu cases requiring hospitalization in those residents aged 65 and older during a two-month period one winter. They matched each case with 2 controls by sex and age (150 cases, 300 controls). They used medical records to determine whether cases and controls had received a flu vaccine shot and whether they had underlying lung disease. They wanted to know whether flu vaccination prevents hospitalization for flu (severe cases of flu). Underlying lung disease is a potential confounder. Example modified from: Stokes, Davis, Koch (2000). “Categorical Data Analysis Using the SAS System,” Chapter 10.

Outcome variable:

IsCase—1=case; 0=control

Predictor variables:

Vaccine—1=vaccinated;0=not

Lung—1=underlying lung disease; 0=no underlying lung disease

Matching variable

Id—identifies each matching group (1 case, 2 controls)

2. Use point-and-click features to create a permanent library that points to the desktop (where the datasets are sitting):

a. Click on “new library” icon (slamming file cabinet on the toolbar).

b. Browse to find your desktop.

c. Name the library lab7.

d. Hit OK to exit and save.

3. Use your explorer browser to find the lab7 library and verify that you have a SAS dataset in there: matched.

4. Use the interactive data analysis features to check the variables in the dataset matched:

a. From the menu select: Solutions(Analysis(Interactive Data Analysis

b. Double click to open: library “lab7”, dataset “matched”

c. Highlight each variable from the menu select: Analyze(Distribution(Y)

d. What things do you notice?

e. What’s your sample size?

5. Run proc freq to view 2x2 tables:

proc freq;

tables iscase*lung iscase*vaccine /nocol nopct;

run;

iscase lung

Frequency‚

Row Pct ‚ 0‚ 1‚ Total

ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ

0 ‚ 252 ‚ 48 ‚ 300

‚ 84.00 ‚ 16.00 ‚

ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ

1 ‚ 87 ‚ 63 ‚ 150

‚ 58.00 ‚ 42.00 ‚

ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ

Total 339 111 450

Table of iscase by vaccine

iscase vaccine

Frequency‚

Row Pct ‚ 0‚ 1‚ Total

ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ

0 ‚ 183 ‚ 117 ‚ 300

‚ 61.00 ‚ 39.00 ‚

ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ

1 ‚ 103 ‚ 47 ‚ 150

‚ 68.67 ‚ 31.33 ‚

ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ

Total 286 164 450

6. Run the INCORRECT model, unconditional logistic regression, to see what happens:

proc logistic data = lab7.matched;

model iscase (event = "1") = lung vaccine /risklimits;

run;

Analysis of Maximum Likelihood Estimates

Standard Wald

Parameter DF Estimate Error Chi-Square Pr > ChiSq

Intercept 1 -0.9430 0.1444 42.6726 ................
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