"Number needed to treat" is a useful epidemiological concept in this case. A perfect drug that cures every single patient has an NNT of one - for each patient you treat, one patient is successfully treated. When you translate the results of SSRI meta-analyses, you get an NNT of about seven - for every seven patients who take SSRIs, one more patient will recover than if those patients were given a placebo.
It's hard to know what to do with that information. Is it worth prescribing antidepressants to 1,000,000 people to treat 142,000 of them? On a pure cost-benefit analysis, the answer is a resounding yes. Antidepressants are relatively cheap and safe. Are you willing to take a pill every day to get a one-in-seven chance of an effective treatment? Maybe. If you're in the throes of a deep depression, you might be willing to give anything a try. If you're just feeling a bit blue, maybe not. By the same token, if you're taking antidepressants and you don't think that they're helping, you're probably right.
Yes. Reviews suggest that statins have an NNT of between 50 and 200 depending on the patient's underlying risk for cardiovascular disease. Coronary stents are probably useless. Reviews of antibiotics, antivirals and corticosteroids show a wide range of NNTs and often show no positive effect.
The drug approval system has a relatively high bar for safety, but a relatively low bar for efficacy. A drug manufacturer only has to prove that their drug is effective for one particular cohort of patients with a particular disease, but doctors are then free to prescribe the drug to any patient with any disease. Many surgical interventions are never subject to a randomised controlled trial.
Here's one:
https://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12...
"SSRIs might have statistically significant effects on depressive symptoms, but all trials were at high risk of bias and the clinical significance seems questionable. SSRIs significantly increase the risk of both serious and non-serious adverse events. The potential small beneficial effects seem to be outweighed by harmful effects."