There is more data, and better data, e.g. data gathered in double-blinded RCTs, that shows things like:
Homeopathy works very well for a variety of conditions, sometimes better than real drugs used to treat them.
Increasing the healthcare budget and the amount of healthcare people receive. Both in rich countries (e.g. USA) and poor ones (India). Having no effect on mortality.
I can make both of these claims based on many individual RCTs, as well as based on the aggregation of all existing RCTs.
I’m not saying that these claims make sense, they don’t, there are critical lenses through which we analyze research. But if you claim to “just follow the data”, and ignore the issue of data quality, selection bias, and fraud… without applying a critical lens, you are lost.
It seems to me like claim (2) could easily make sense if you interpret it more charitably as “the mortality effects are too small for the studies to detect”. I don’t have a particularly strong prior that marginal healthcare spending is all that useful for increasing life expectancy—diminishing returns can mean that the average dollar spent on healthcare does much more than the marginal dollar.
Can you justify your claim that (2) does not make sense?
It seems to me like claim (2) could easily make sense if you interpret it more charitably as “the mortality effects are too small for the studies to detect”.
Bingo, partially, it’s likely that at least in the Indian study the mortality was too low over that period to be accurately represented … which is the same argument I’d have for 100% of the kidney donation studies, follow-up is not lengthy enough, and the longer you followup and the stronger your controls the worse things get.
Death is a bad endpoint for evaluating things and thus we should not be using it.
I would have a longer claim (in the linked article) that in some cases it is worth using, given that e.g. our views around why modern medicine is good and worthwhile ultimately root themselves in preventing mortality and such things are as of yet on shaky grounds.
But when doing risk estimates we should try looking at proxies for mortality and QAL downgrades as opposed to mortality, especially when we don’t have life-long studies or studies following people into old age when most of them start dying.
It seems to me like claim (2) could easily make sense if you interpret it more charitably as “the mortality effects are too small for the studies to detect”. I don’t have a particularly strong prior that marginal healthcare spending is all that useful for increasing life expectancy—diminishing returns can mean that the average dollar spent on healthcare does much more than the marginal dollar.
Can you justify your claim that (2) does not make sense?
Bingo, partially, it’s likely that at least in the Indian study the mortality was too low over that period to be accurately represented … which is the same argument I’d have for 100% of the kidney donation studies, follow-up is not lengthy enough, and the longer you followup and the stronger your controls the worse things get.
Death is a bad endpoint for evaluating things and thus we should not be using it.
I would have a longer claim (in the linked article) that in some cases it is worth using, given that e.g. our views around why modern medicine is good and worthwhile ultimately root themselves in preventing mortality and such things are as of yet on shaky grounds.
But when doing risk estimates we should try looking at proxies for mortality and QAL downgrades as opposed to mortality, especially when we don’t have life-long studies or studies following people into old age when most of them start dying.