Example 1 for PROC MULTTEST

The script first creates a dataset 'a' representing animals, where S1 and S2 are tumor types and Dose is the treatment group. Then, it uses PROC MULTTEST to perform a one-sided upper Cochran-Armitage test to detect a linear trend in tumor incidence as a function of dose. Statistical significance is evaluated via a permutation method with 10,000 resamples to correct p-values. The permutation results are stored in a 'pmt' dataset which is then displayed.

Data Analysis

Type : CREATION_INTERNE

The data representing animals and tumors are created directly in the script using a DATA step and a 'datalines' statement.

1 Code Block

DATA STEP Data

Explanation :
This block creates the 'a' table in memory. Variables S1 and S2 represent two tumor types (0=absence, 1=presence) and 'Dose' is the grouping variable. Data is integrated directly into the code via 'datalines'.

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1	title 'Cochran-Armitage Test with Permutation Resampling';
2
3	DATA a;
4	INPUT S1 S2 Dose;
5	DATALINES;
6	0 1 1
7	1 0 1
8	0 1 1
9	0 1 1
10	0 1 1
11	1 0 1
12	1 0 2
13	1 0 2
14	0 1 2
15	1 0 2
16	0 1 2
17	1 0 2
18	1 0 3
19	1 0 3
20	1 0 3
21	0 1 3
22	0 1 3
23	1 0 3
24	;
25	RUN;

2 Code Block

PROC MULTTEST Data

Explanation :
This procedure applies multiple tests to the data in table 'a'. It performs a Cochran-Armitage test (ca) for a linear trend on variables S1 and S2, using 'Dose' as the class variable. Significance is calculated by a permutation method (10,000 samples) to adjust p-values. Permutation results are saved in the 'pmt' table.

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1	PROC MULTTEST DATA=a permutation nsample=10000 seed=36607 outperm=pmt;
2	test ca(S1 S2 / permutation=10 uppertailed);
3	class Dose;
4	contrast 'CA Linear Trend' 0 1 2;
5	RUN;

3 Code Block

PROC PRINT

Explanation :
This block displays the content of the 'pmt' table, which was generated by the previous PROC MULTTEST step and contains the permutation results.

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1	PROC PRINT DATA=pmt;
2	RUN;

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