Download Regression Models for Binary Outcomes Using SAS

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Analysis of Maximum Likelihood Estimates
Standard
Wald
DF
Estimate
Error
Chi-Square
Parameter
Intercept
SHOKTYPE
SHOKTYPE
SHOKTYPE
SHOKTYPE
SHOKTYPE
SBP1
3
4
5
6
7
1
1
1
1
1
1
1
-0.0399
2.1113
2.0243
1.2458
1.5265
2.8397
-0.0184
1.1654
0.8214
0.7886
0.8328
0.8284
0.9450
0.00833
Pr > ChiSq
0.0012
6.6064
6.5896
2.2378
3.3956
9.0298
4.8641
0.9727
0.0102
0.0103
0.1347
0.0654
0.0027
0.0274
Odds Ratio Estimates
Point
Estimate
Effect
SHOKTYPE
SHOKTYPE
SHOKTYPE
SHOKTYPE
SHOKTYPE
SBP1
3
4
5
6
7
vs
vs
vs
vs
vs
2
2
2
2
2
8.259
7.570
3.476
4.602
17.111
0.982
95% Wald
Confidence Limits
1.651
1.614
0.679
0.907
2.685
0.966
41.317
35.510
17.781
23.341
109.061
0.998
Association of Predicted Probabilities and Observed Responses
Percent Concordant
77.9
Somers' D
0.560
Percent Discordant
21.9
Gamma
0.561
Percent Tied
0.2
Tau-a
0.268
Pairs
2924
c
0.780
The commands to run the equivalent model using Proc Genmod are shown below (output
not displayed).
/*RUN THE LOGISTIC REGRESSION USING PROC GENMOD*/
title "Logistic Regression Using Proc Genmod";
proc genmod data=sasdata2.afifi descending;
class shoktype(ref="2") / param=ref;
model died = SHOKTYPE sbp1 /dist=bin type3;
run;
9
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