In this case it really is not evidence of an absence of an effect. In fact it should increase your belief that the treatment has an effect (e.g. if you did a Ba

In this case it really is not evidence of an absence of an effect. In fact it should increase your belief that the treatment has an effect (e.g. if you did a Bayesian analysis), since the treated group did have lower death and ICU usage than the untreated. However this evidence does not rise to the 1% or 5% that we traditionally use, so it's fair to conclude that we don't know if the drug works or not. This experiment is so small that it had virtually no chance of ever being able to detect an effect on the probability of death (what statisticians call the "statistical power" of the experiment is only 10% whereas a well designed study should aim for power above 80%). Even if the drug cured 100% of people it was given to, a study this small would not be able to statistically conclude whether the drug works. Since the baseline death rate is only 4%, it takes fairly large samples to be able to detect any change. A 200 person experiment has basically no hope of being able to detect a difference in the death endpoint (there is a bit more hope for the more common ICU endpoint).