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Journal of Chinese Integrative Medicine ›› 2010, Vol. 8 ›› Issue (1): 90-94.doi: 10.3736/jcim20100117

• Medical Statistice • Previous Articles     Next Articles

Statistical analysis for data of multidimensional contingency table with SAS software package (Part three)

 Yi Gea, Liang-ping Hub   

  1. a Command Academy of Logistics, Beijing 100858, China
    b Consulting Center of Biomedical Statistics, Academy of Military Medical Sciences, Beijing 100850, China
  • Received:2009-12-05 Accepted:2009-12-20 Online:2010-01-20 Published:2010-01-15
  • Contact: Liang-ping Hu E-mail:lphu812@sina.com

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Key words: Statistics, Medicine, Data analysis, Statistical, Qualitative data, SAS software

"

孕妇年龄（A） 吸烟（B） 服避孕药（C） 健康状况（D） 人数
正常 不正常
<30岁 是 是 204 58
否 330 67
否 是 1 051 210
否 1 014 178
30~35岁 是 是 125 31
否 180 42
否 是 582 144
否 489 85
>35岁 是 是 35 20
否 35 10
否 是 158 53
否 119 31

Testing global null hypothesis: BETA=0"

Test Chi-square df Pr>ChiSq
Likelihood ratio 31.156 2 5 <0.000 1
Score 32.372 7 5 <0.000 1
Wald 31.932 9 5 <0.000 1

Analysis of maximum likelihood estimates"

Parameter df Estimate Standard error Wald chi-square Pr>ChiSq
Intercept 1 1.909 4 0.147 3 168.014 6 <0.000 1
A 1 –0.129 2 0.089 7 2.072 7 0.15
B 1 –0.201 9 0.214 6 0.885 0.346 8
C 1 –0.061 8 0.183 4 0.113 5 0.736 1
A*B 1 –0.014 5 0.126 3 0.013 2 0.908 7
A*C 1 –0.111 3 0.109 1 1.040 6 0.307 7

Testing global null hypothesis: BETA=0"

Test Chi-square df Pr>ChiSq
Likelihood ratio 30.110 9 3 <0.000 1
Score 30.554 9 3 <0.000 1
Wald 30.337 5 3 <0.000 1

Analysis of maximum likelihood estimates"

Parameter df Estimate Standard error Wald chi-square Pr>ChiSq
Intercept 1 –2.011 2 0.100 2 402.828 7 <0.000 1
A 1 0.196 6 0.053 6 13.439 2 0.000 2
B 1 0.225 1 0.085 6 6.918 2 0.008 5
C 1 0.233 5 0.073 3 10.136 4 0.001 5

Odds ratio estimates"

Effect Point estimate 95% Wald confidence limits
A 1.217 1.096 1.352
B 1.252 1.059 1.481
C 1.263 1.094 1.458

Class level information"

Class Value Design variables
A 1 1 0
2 0 1
3 0 0
B 0 1
1 0
C 0 1
1 0

Analysis of maximum likelihood estimates"

Parameter df Estimate Standard error Wald chi-square Pr>ChiSq
Intercept 1 –0.842 6 0.131 2 41.223 5 <0.000 1
A 1 1 –0.489 3 0.118 6 17.007 5 <0.000 1
A 2 1 –0.394 3 0.125 5 9.862 2 0.001 7
B 0 1 –0.226 6 0.085 6 7.003 4 0.008 1
C 0 1 –0.232 9 0.073 4 10.074 7 0.001 5

Odds ratio estimates"

Effect Point estimate 95% Wald confidence limits
A 1 vs 3 0.613 0.486 0.774
A 2 vs 3 0.674 0.527 0.862
B 0 vs 1 0.797 0.674 0.943
C 0 vs 1 0.792 0.686 0.915

Type 3 analysis of effects"

Effect df Wald chi-square Pr>ChiSq
Treatment 2 12.692 8 0.001 8
Sex 1 5.301 3 0.021 3
Age 1 7.631 4 0.005 7

Analysis of maximum likelihood estimates"

Parameter df Estimate Standard error Wald chi-square Pr>ChiSq
Intercept 1 15.866 9 6.405 6 6.135 7 0.013 2
Treatment A 1 3.179 0 1.013 5 9.837 5 0.001 7
Treatment B 1 3.726 4 1.133 9 10.800 6 0.001 0
Sex F 1 1.823 5 0.792 0 5.301 3 0.021 3
Age 1 –0.265 0 0.095 9 7.631 4 0.005 7

Odds ratio estimates"

Effect Point estimate 95% Wald confidence limits
Treatment A vs P 24.022 3.295 175.121
Treatment B vs P 41.528 4.500 383.262
Sex F vs M 6.194 1.312 29.248
Age 0.767 0.636 0.926
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