# chi-squared / Fisher's exact test with incomplete data for contingency table

I have a treatment group (n=42) and a control group (N=12). From this this, 13 used in the treatment HTN drugs and 4 people in control group used HTN drugs. How should the contingency table be set up? Is the chi-squared / Fisher's exact possible in this case as the 13 and 4 do not equal the total?

I assume that there are only two possibilities for HTN drug use (namely, "yes" and "no"). Thus, there would have to be $42-13=29$ non-drug users in the treatment group, for example. It is straightforward to set up a contingency table with your data in this way (this example uses R):

c.table = rbind(c(13, 42-13),
c( 4, 12- 4) )
colnames(c.table) = c("yes", "no")
rownames(c.table) = c("treatment", "control")
c.table
#           yes no
# treatment  13 29
# control     4  8
chisq.test(c.table, simulate.p.value=T)
#
#        Pearson's Chi-squared test with simulated p-value (based on 2000
#         replicates)
#
# data:  c.table
# X-squared = 0.0245, df = NA, p-value = 1


Because the odds ratio is very close to $1$ (viz. $0.9$) and you have relatively few data, the p-value is high, but this is certainly doable.