They were lies based on falsified evidence it seems yet passed the peer review stage.
Other trials were stopped on the back of these "studies" maybe leading directly to excess deaths..
What should us informed laypeople think of scientists if this is how they publish?
Apologies but the answer is again - Science is difficult
There are two issues which you've mentioned
How can a paper, where the data have been tampered with, pass peer review? I haven't checked this in detail for the case you mention, so my comments are general. This is a feature of peer review which assumes that scientist's data are accurate. When data are falsified with, then peer review might not pick it up, unless it is very obvious. This is far from perfect and I understand that you will say this ludicrous. However this is close to impossible to change. As a reviewer you cannot repeat the work in most cases, your review is based on scrutinising the methods, the results and their interpretation, but it assumes the raw data are accurate. This is why I always stress that the ultimate check on any result is its reproducibility by other scientists. One off results can go badly wrong. We all know what happened when Angel Keys "found" a link between fat in diets and heart attacks. While unfortunately there are cases of scientists fabricating data, this does not happens often as there is usually no incentive for scientists to do this and ultimately it will be found out.
There is one exception and it is a big issue, namely when companies are involved, who have economic reasons for a certain outcome of studies. Many excellent scientists work for pharmaceutical companies. In these cases it is important to apply methods and procedures which try to prevent biasing results, e.g. when measuring the efficacy of drugs. It used to be that negative results were not reported thus clearly leading to an overestimate of how well a medication will work. Scientists also try to prevent having their own opinions sub-consciously influence their results. For example when scientists analyse data to make very precise measurements, they "blind" their data, i.e. they do the full analysis without looking at the region of interest or multipling the result by a random number. Once all details including uncertainties are understood, the signal will be unblinded and only then the result will become visible to the scientists themselves.
The second point is an ethical question. To illustrate this, assume that you are conducting a long-term study with a drug suppressing a cancer, e.g. you have patients with leukemia, half are given the medicament the other half not. After only a few months you realise that the drug works extremely well and all patients taking the drug have normal while blood cells again. Thus you decide to stop the study and allow all patients access to this drug. This question always arises when patients well-being depend upon. Covid makes it worse in that decision have to be (or are) taken before results are supported by other studies or maybe even before peer review. These decisions can be painful, and can have bad outcomes, in particular if the result on which it is based does not hold up to scrutiny. However blaming others for such decisions taken as in the case you mentioned is not helpful.
