Uncontrolled data is difficult to interpret given numerous confounding variables and limitations of the study parameters. Did the 8 patients lost to follow up die? Are the several patients that still haven't been discharged from the hospital in the study going to definitively survive? Did they just happen to pick a group that had better odds of survival? Without randomized control groups it is difficult to say.
Unfortunately there appear to also be some issues with Gilead's RCT for remdesivir. It appears that after interim analysis they decided mid study to increase the N from 400 to 2400 and change their primary outcomes which is a bit questionable, but potentially indicates that treatment effect is much less than what was expected. There are legitimate reasons to change primary outcome and number of patients mid-trial, but given that pharma companies are for profit and we are in the middle of a pandemic and are needing answers quickly, I have a hard time believing this was done for any other reason than because they didn't expect a positive outcome with their current study design, so increased N, added additional treatment arms, and picked a softer target in order to pick up smaller treatment effect and have better powered sub group analysis which might soften the blow of a negative trial if sub group analysis demonstrates potential populations that might benefit.
I'm hoping we find something, and fast, but I am just not convinced that any of these repurposed drugs are going to be significantly effective in preventing death or significant morbidity. There is just such a long list of failed or minimally effective anti-virals, that it is hard not to be a bit pessimistic. Nevertheless, I would be happy to be proven wrong, and I think until we have more definitive data to say it does/doesn't work, we should keep it as an option in our sickest patients.
EDIT: Completely missed that they excluded any patients that died within 24 hours of treatment initiation which is incredibly sketchy and leads to what is called survivorship bias.
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