I can’t actually access the full paper (either via university access, or Anna’s Archive/sciDB), so I can’t comment on specifics, but their extended abstract mentions that that they used “Cox proportional hazards models, general linear regression, and Poisson regression models were applied to assess the associations between red meat intake and different cognitive outcomes.”
Speaking as a biochemist (i.e. someone well versed in reading scientific papers from the life sciences, but who does not have particular training or experience with the quite different context of clinical medicine research), it looks fairly legit, in that those statistical methods are typical of what I’d expect for something like this. That’s vague, but it passes the sniff test, I suppose.
I was initially dubious of the journal/research on the basis of being unable to access the paper (and not knowing anything of this journal), but I feel comfortable in dismissing those concerns after have a wee gander at the journal itself (it seems fairly prominent and well respected). Having not read the paper (nor being familiar with this specific area of research), I am far less able to judge the paper itself, but at least it’s not a case of dismissing the research outright because of the journal being sus.
I can’t actually access the full paper (either via university access, or Anna’s Archive/sciDB), so I can’t comment on specifics, but their extended abstract mentions that that they used “Cox proportional hazards models, general linear regression, and Poisson regression models were applied to assess the associations between red meat intake and different cognitive outcomes.”
Speaking as a biochemist (i.e. someone well versed in reading scientific papers from the life sciences, but who does not have particular training or experience with the quite different context of clinical medicine research), it looks fairly legit, in that those statistical methods are typical of what I’d expect for something like this. That’s vague, but it passes the sniff test, I suppose.
I was initially dubious of the journal/research on the basis of being unable to access the paper (and not knowing anything of this journal), but I feel comfortable in dismissing those concerns after have a wee gander at the journal itself (it seems fairly prominent and well respected). Having not read the paper (nor being familiar with this specific area of research), I am far less able to judge the paper itself, but at least it’s not a case of dismissing the research outright because of the journal being sus.
Without knowing your background, it’s hard to gauge whether this explainer on some of the stats methods mentioned above would be appreciated, but here you go, just in case.