Friday, July 27, 2007
No, and No.
Puritans and busybodies everywhere are crowing about a meta-study suggesting that pot smokers are more likely to develop psychotic disorders later in life. The claim, boasted by newspapers, PR mouthpieces, and even study authors, is that pot causes these disorders. While that conclusion is possible, the data don't actually do anything o support it. You see, people predisposed to psychosis could be drawn to pot for the same (or different) reasons that they are predisposed to psychosis.
Take cigarettes: no one has ever alleged that they cause psychosis, but schizophrenics tend to be HEAVY smokers. Before and certainly after developing the disease. Analysis similar to the current pot study would similarly link tobacco to psychosis, an association known to be inverted (smoking is a form of self-medication for many schizophrenics).
/rant.
Take cigarettes: no one has ever alleged that they cause psychosis, but schizophrenics tend to be HEAVY smokers. Before and certainly after developing the disease. Analysis similar to the current pot study would similarly link tobacco to psychosis, an association known to be inverted (smoking is a form of self-medication for many schizophrenics).
/rant.
Labels: causality, drugs, marijuana, pot, research methods, science, stupidity
Tuesday, January 23, 2007
Pet Peeve
I've been reading the text for my Epidemiologic Methods class this semester, and so far I really like it. First and foremost, it is so infinitely better than last semester's unedited monstrosity (which doesn't even dignify a link) that I was nearly elated to read it from the start. But then, almost 200 pages in, it begins to address one of my biggest research peeves EVER: the absolute preeminence of the P-value for absolutely everything.
For gushier stuff than T-cell counts (like cognitive function scores or pain indexes), where measures are necessarily subjective, I would argue that such dichotomization is totally inappropriate. Sadly, that's all that gets published. Unless you're already a super-established expert with bottomless funds who's probably sleeping with the editor.
...type I and type II errors arise because the investigator has attempted to dichotomize the results of a study into the categories "significant" and "not significant." Since this degradation of the study is unnecessary, an "error" that results from an incorrect classification of the study result is also unnecessary.As one who falls squarely on the quantitative side of things - scientifically, what you think is only as interesting to me as the numbers you can show - the reliance on P irks me for just this reason. If you see 15 separate studies of, say, Drug X on T-cell counts, showing a (non-significant) p of .1, to me that's nearly as interesting as two with p=.003.
For gushier stuff than T-cell counts (like cognitive function scores or pain indexes), where measures are necessarily subjective, I would argue that such dichotomization is totally inappropriate. Sadly, that's all that gets published. Unless you're already a super-established expert with bottomless funds who's probably sleeping with the editor.
Labels: epidemiology, rant, research methods, statistics, textbooks
