Kreara

Cochran-Armitage Trend Test
Prepared by Sreeja E V

In clinical trials, it is often of interest to investigate the relationship between the increasing dosage and the effect of the drug under study. Usually the dose levels tested are ordinal, and the effect of the drug is measured in binary. In such cases, Cochran-Armitage trend test is most frequently used to test for trend.

Here, the Null hypothesis (H0): There is no linear trend in effect of the drug under study across increasing levels of dosage. Alternative hypothesis (H1): There is linear trend in effect of the drug under study across increasing levels of dosage.

Consider an example. The data set effect contains hypothetical data for a clinical trial of a case control study. The clinical trial investigates whether the variable cascon relates with different genotype statuses. Subjects have one of either three genotype statuses 1, 2 or 3 where 1 represents abnormal, 2 represents partially abnormal and 3 represents normal status. The variable cascon has values 1=’Case’ and 2=’Control’. The number of subjects for each group is represented by the variable Count.

data effect;
input status cascon Count @@;
datalines;
1 1 15 1 2 26
2 1 19 2 2 10
3 1 20 3 2 3
;
run;

proc freq data=effect;
tables status*cascon / trend measures cl;
weight Count;
title 'Clinical Trial for case control study’;
run;

The output will appear as follows

Cochran-Armitage Trend Test
*************************

Statistic (Z) 4.0252
One-sided Pr > Z <.0001

Two-sided Pr > Z <.0001

Sample Size = 93

We consider the two-sided -value tests against either an increasing or decreasing alternative. Using trend test we get Pr > Z <.0001. Thus the null hypothesis is rejected and it is concluded that there exists a trend in binomial proportions of response across gene status.