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Journal of Chinese Integrative Medicine ›› 2011, Vol. 9 ›› Issue (7): 711-714.doi: 10.3736/jcim20110703

• Methodology • Previous Articles     Next Articles

Methods and analysis of realizing randomized grouping

Liang-ping Hu(), Xiao-lei Bao, Qi Wang   

  1. Consulting Center of Biomedical Statistics, Academy of Military Medical Sciences, Beijing 100850, China
  • Received:2011-05-11 Accepted:2011-05-16 Online:2011-07-20 Published:2011-06-15

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Randomization is one of the four basic principles of research design. The meaning of randomization includes two aspects: one is to randomly select samples from the population, which is known as random sampling; the other is to randomly group all the samples, which is called randomized grouping. Randomized grouping can be subdivided into three categories: completely, stratified and dynamically randomized grouping. This article mainly introduces the steps of complete randomization, the definition of dynamic randomization and the realization of random sampling and grouping by SAS software.

Key words: statistics,medical, research design, random allocation, SAS program

"

Important non-experimental factors Score
Experimental group Control group Absolute difference
Disease state
Mild 0 1 1
Medium 1 0 1
Severe 0 0 0
Disease duration
Short 0 1 1
Long 1 0 1
Daily exercise amount
Small 1 1 0
Large 0 0 0
Total 3 3 4*

"

Important non-
experimental factors		 Score
Experimental
group		 Control
group		 Absolute
difference
Disease state
Mild 0 1 1
Medium 1 0 1
Severe 1 0 1
Disease duration
Short 1 1 0
Long 1 0 1
Daily exercise amount
Small 1 1 0
Large 1 0 1
Total 6 3 5

"

Important non-
experimental factors		 Score
Experimental
group		 Control
group		 Absolute
difference
Disease state
Mild 0 1 1
Medium 1 0 1
Severe 0 1 1
Disease duration
Short 0 2 2
Long 1 0 1
Daily exercise amount
Small 1 1 0
Large 0 1 1
Total 3 6 7

"

proc plan;
factors i=100;
output out=a;
run;
data b c;
set a;
mouse=_n_;
if i<=50 then do;
group=‘甲’; output b;drop i;
end;
else do;
group=‘乙’; output c;drop i;
end;
run;		 data d;
set b c;
run;
ods html;
proc print noobs;
run;
ods html close;





"

Mouse Group Mouse Group Mouse Group Mouse Group Mouse Group
2 A 44 A 84 A 19 B 61 B
4 A 45 A 85 A 20 B 63 B
… … … … … … … … … …
39 A 81 A 17 B 57 B 99 B
42 A 82 A 18 B 58 B 100 B

"

Patient
number		 Gender Age
(years)		 Patient
number		 Gender Age
(years)
1 Female 60 11 Male 58
2 Female 64 12 Male 63
3 Male 37 13 Female 23
4 Female 57 14 Female 37
5 Female 41 15 Female 20
6 Female 31 16 Female 33
7 Male 60 17 Female 39
8 Male 64 18 Male 40
9 Male 58 19 Female 49
10 Male 16 20 Female 42

"

data a;
input id sex$ age;
cards;
1 F 60
2 F 64
… … …
20 F 42
;
run;		 ods html;
proc surveyselect data=a method=srs n=10 out=b;
run;
proc print data=b;
run;
ods html close;

"

Selection method Simple random sampling
Input data set A
Random number seed 937359000
Sample size 10
Selection probability 0.5
Sampling weight 2
Output data set B

"

Objects ID Gender Age (years)
1 5 Female 41
2 6 Female 31
3 10 Male 16
4 11 Male 58
5 12 Male 63
6 13 Female 23
7 15 Female 20
8 16 Female 33
9 19 Female 49
10 20 Female 42
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[2] Hu LP. Research design and statistical analysis of laboratory medicine[M]. Beijing: People’s Military Medical Press, 2004: 4-8
胡良平 . 检验医学科研设计与统计分析[M]. 北京: 人民军医出版社, 2004: 4-8
[1] Liang-ping Hu, Xiao-lei Bao, Chen-long Lv. Two-factor designs unable to examine the interactions (Part 2). Journal of Chinese Integrative Medicine, 2012, 10(9): 966-969.
[2] Liang-ping Hu, Xiao-lei Bao, Chen-yi Guo . Two-factor designs unable to examine the interactions (Part 1). Journal of Chinese Integrative Medicine, 2012, 10(8): 853-857.
[3] Liang-ping Hu , Xiao-lei Bao, Chen-long Lü . How to choose an appropriate experimental design type (Part 2). Journal of Chinese Integrative Medicine, 2012, 10(7): 738-742.
[4] Liang-ping Hu, Xiao-lei Bao. How to choose an appropriate experimental design type (Part 1). Journal of Chinese Integrative Medicine, 2012, 10(6): 615-618.
[5] Liang-ping Hu, Xiao-lei Bao, Qi Wang . The balance principle in scientific research. Journal of Chinese Integrative Medicine, 2012, 10(5): 504-507.
[6] Liang-ping Hu, Xiao-lei Bao, Xue Guan . Estimation of sample size and testing power (Part 7). Journal of Chinese Integrative Medicine, 2012, 10(4): 380-383.
[7] Hu Liang-ping, Bao Xiao-lei, Guan Xue, Zhou Shi-guo. Estimation of sample size and testing power (Part 6). Journal of Chinese Integrative Medicine, 2012, 10(3): 298-302.
[8] Hu Liang-ping, Bao Xiao-lei, Guan Xue, Zhou Shi-guo. Estimation of sample size and testing power (Part 5). Journal of Chinese Integrative Medicine, 2012, 10(2): 154-159.
[9] Xiao-lei Bao, Liang-ping Hu. Multifactor designs able to examine the interactions. Journal of Chinese Integrative Medicine, 2012, 10(12): 1371-1374.
[10] Liang-ping Hu, Xiao-lei Bao. Three-factor designs unable to examine the interactions (Part 2). Journal of Chinese Integrative Medicine, 2012, 10(11): 1229-1232.
[11] Liang-ping Hu, Xiao-lei Bao. Three-factor designs unable to examine the interactions (Part 1). Journal of Chinese Integrative Medicine, 2012, 10(10): 1088-1091.
[12] Hu Liang-ping, Bao Xiao-lei, Guan Xue, Zhou Shi-guo. Estimation of sample size and testing power (Part 4). Journal of Chinese Integrative Medicine, 2012, 10(1): 35-38.
[13] Liang-ping Hu, Xiao-lei Bao, Qi Wang . The repetition principle in scientific research. Journal of Chinese Integrative Medicine, 2011, 9(9): 937-940.
[14] Liang-ping Hu, Xiao-lei Bao, Qi Wang . The control principle in scientific research. Journal of Chinese Integrative Medicine, 2011, 9(8): 834-.
[15] Liang-ping Hu, Xiao-lei Bao, Qi Wang. The principle of randomization in scientific research. Journal of Chinese Integrative Medicine, 2011, 9(6): 592-595.
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