|
Marginal Logistic Regression Model for Obesity |
|
Muscatine Coronary Risk Factor Study |
|
The GENMOD Procedure |
|
Model Information |
|
|
Data Set |
WORK.OBESITY |
|
Distribution |
Binomial |
|
Link Function |
Logit |
|
Dependent Variable |
y |
|
Observations Used |
9856 |
|
Missing Values |
4712 |
|
Class Level Information |
||
|
Class |
Levels |
Values |
|
id |
4856 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 ... |
|
occasion |
3 |
1 2 3 |
|
Response Profile |
||
|
Ordered |
y |
Total |
|
1 |
1 |
2112 |
|
2 |
0 |
7744 |
|
PROC GENMOD is modeling the probability that y='1'. |
|
Parameter Information |
|
|
Parameter |
Effect |
|
Prm1 |
Intercept |
|
Prm2 |
gender |
|
Prm3 |
cage |
|
Prm4 |
cage2 |
|
Prm5 |
gender*cage |
|
Prm6 |
gender*cage2 |
|
Criteria For Assessing Goodness Of Fit |
|||
|
Criterion |
DF |
Value |
Value/DF |
|
Deviance |
9850 |
10185.6781 |
1.0341 |
|
Scaled Deviance |
9850 |
10185.6781 |
1.0341 |
|
Pearson Chi-Square |
9850 |
9848.8851 |
0.9999 |
|
Scaled Pearson X2 |
9850 |
9848.8851 |
0.9999 |
|
Log Likelihood |
|
-5092.8391 |
|
|
Algorithm converged. |
|
Analysis Of Initial Parameter Estimates |
|||||||
|
Parameter |
DF |
Estimate |
Standard Error |
Wald 95% Confidence Limits |
Chi-Square |
Pr > ChiSq |
|
|
Intercept |
1 |
-1.2130 |
0.0461 |
-1.3034 |
-1.1227 |
692.24 |
<.0001 |
|
gender |
1 |
0.0962 |
0.0647 |
-0.0305 |
0.2229 |
2.21 |
0.1368 |
|
cage |
1 |
0.0324 |
0.0128 |
0.0073 |
0.0575 |
6.42 |
0.0113 |
|
cage2 |
1 |
-0.0183 |
0.0039 |
-0.0260 |
-0.0106 |
21.83 |
<.0001 |
|
gender*cage |
1 |
-0.0043 |
0.0178 |
-0.0392 |
0.0306 |
0.06 |
0.8105 |
|
gender*cage2 |
1 |
0.0037 |
0.0055 |
-0.0070 |
0.0144 |
0.46 |
0.4954 |
|
Scale |
0 |
1.0000 |
0.0000 |
1.0000 |
1.0000 |
|
|
|
|
GEE Model Information |
|
|
Log Odds Ratio Structure |
Fully Parameterized Clusters |
|
Within-Subject Effect |
occasion (3 levels) |
|
Subject Effect |
id (4856 levels) |
|
Number of Clusters |
4856 |
|
Clusters With Missing Values |
3086 |
|
Correlation Matrix Dimension |
3 |
|
Maximum Cluster Size |
3 |
|
Minimum Cluster Size |
1 |
|
Log Odds Ratio Parameter Information |
|
|
Parameter |
Group |
|
Alpha1 |
(1, 2) |
|
Alpha2 |
(1, 3) |
|
Alpha3 |
(2, 3) |
|
Algorithm converged. |
|
Analysis Of GEE Parameter Estimates |
||||||
|
Empirical Standard Error Estimates |
||||||
|
Parameter |
Estimate |
Standard Error |
95% Confidence Limits |
Z |
Pr > |Z| |
|
|
Intercept |
-1.2135 |
0.0506 |
-1.3126 |
-1.1144 |
-24.00 |
<.0001 |
|
gender |
0.1159 |
0.0711 |
-0.0235 |
0.2553 |
1.63 |
0.1033 |
|
cage |
0.0378 |
0.0133 |
0.0118 |
0.0638 |
2.85 |
0.0043 |
|
cage2 |
-0.0175 |
0.0034 |
-0.0241 |
-0.0109 |
-5.19 |
<.0001 |
|
gender*cage |
0.0075 |
0.0182 |
-0.0282 |
0.0433 |
0.41 |
0.6795 |
|
gender*cage2 |
0.0039 |
0.0046 |
-0.0051 |
0.0130 |
0.85 |
0.3949 |
|
Alpha1 |
3.1528 |
0.1280 |
2.9019 |
3.4037 |
24.63 |
<.0001 |
|
Alpha2 |
2.5975 |
0.1353 |
2.3323 |
2.8627 |
19.20 |
<.0001 |
|
Alpha3 |
2.9868 |
0.1236 |
2.7446 |
3.2291 |
24.17 |
<.0001 |
|
Wald Statistics For Type 3 GEE Analysis |
|||
|
Source |
DF |
Chi-Square |
Pr > ChiSq |
|
gender |
1 |
2.65 |
0.1033 |
|
cage |
1 |
8.13 |
0.0043 |
|
cage2 |
1 |
26.92 |
<.0001 |
|
gender*cage |
1 |
0.17 |
0.6795 |
|
gender*cage2 |
1 |
0.72 |
0.3949 |
|
Contrast Results for GEE Analysis |
||||
|
Contrast |
DF |
Chi-Square |
Pr > ChiSq |
Type |
|
Age X Gender Interaction |
2 |
0.91 |
0.6356 |
Wald |