I think a simple chi-squared test will do the trick. Do you have 1,000,000 observations for both control and test? If so, your table of observations will be (in R code)

Edit: Woops! Left of a zero!

m <- rbind(c(3, 1000000-3), c(10, 1000000-10))
#      [,1]   [,2] 
# [1,]    3 999997
# [2,]   10 999990

And chi-squared test will be

chisq.test(m)

Which returns chi-squared = 2.7692, df = 1, p-value = 0.0961, which is not statistically significant at the p < 0.05 level. I'd be surprised if these could be clinically significant anyway.

I think a simple chi-squared test will do the trick. Do you have 1,000,000 observations for both control and test? If so, your table of observations will be (in R code)

Edit: Woops! Left off a zero!

m <- rbind(c(3, 1000000-3), c(10, 1000000-10))
#      [,1]   [,2] 
# [1,]    3 999997
# [2,]   10 999990

And chi-squared test will be

chisq.test(m)

Which returns chi-squared = 2.7692, df = 1, p-value = 0.0961, which is not statistically significant at the p < 0.05 level. I'd be surprised if these could be clinically significant anyway.