On Mon, 3 May 2021 17:56:41 -0700 (PDT), Cosine <ase...@gmail.com
I'm more than a little baffled at what Cosine is really looking
for in an answer. David Jones has provided one sort of answer -
Does that one satisfy?
Here is a more philosophical approach.
> What are the sources causing statistical correlation or dependence?
>What are the characteristics/factors of these sources in common?
This is what the sciences are about, finding correlations and
dependence and trying to describe "causation".
>More directly, given a particular situation, how do we identify these sources?
There are a whole lot of sciences, which each have their
own tools. Astrophysicists work rather differently from
> Let's use the human trial as an example.
>A well-known example for eliminating the potential sources of correlation when testing the efficacy of a new drug for skin is to use the two hands of the same person as testing and control groups. Then we recruit enough persons to form the sample groups.
> This example implies that the sources of correlation exist even in the same person.
It is KNOWN that age and sex are important in many human
responses, in addition to whatever else might matter as between-
person differences. Using each person as their own control
effectively eliminates those sources of separate causation from
the inference when looking at the quantitative differences of results.
> Strangely, when we test a drug for another purpose, say, for treating headache, we form the testing and control groups by recruiting persons to each of the two groups. Why could we be sure that there are no sources of correlation in the same person for this case?
"...no sources of correlation in the same person" is a phrase that
eludes my understanding.
"Crossover designs" do make use of the same person for control
when looking at the headache remedies you imagine.
A trial might go a step beyond "randomizing" to use a "stratified-
random" assignment to groups, if the PIs expect that (say) age and
sex might matter for outcome. That "matches" the characteristics
of groups, to elimiinate the source of variation on an ANOVA.
Lesser factors that are suspected to have a relation to outcome
might be "controlled for" by including covariates in the analysis.
Including covariates is often (far) preferable to the use of "matched
cases" when the matching is not as precise as "same person".
- I was alarmed by a study that analysed by paired-cases when
the matching was "within four years of age". That might seem close
enough as a logicial proposition in a classroom, except that the
disease was "childhood leukemia", age range of maybe 12 years.