How does strong includingcriteria in rct's reflect on the power of that rct?

#1
Hello!
Could anyone explain to me why researchers use strong criteria in rct's and what then happens to the power?

Thanks in advance!
 
#3
Hai Karabiner,

It was in an assignment I did a few weeks ago but I still cant get my mind around it.

By strong criteria I mean no comorbidity.

I was here at the university of Padua but since we don't really have a good chatroom I cant really ask my fellow students for help. Thats why Im here.

Greetings,

Chicken
 

Karabiner

TS Contributor
#4
Do you perhaps mean exclusion criteria?
If we stick to comorbidity as an example, then excluding comorbid patients would mean
that factors are excluded which would (probably) not be affected by the treatment. But
these factors can independenttly affect the outcome, e.g. symptom severity. Now the
problem is, every factor independently affecting the outcome will increase variablilty of
the outcome across treatments (since allocation to groups is randomized, comborbidity
will evenly be distributed across groups). This additional variabilty will be independent
of treatment effects. So you will have more "random noise" in your outcome, which
means that difficulty increases to detect the "signal" (the treatment effect). The probabilty
to detect the signal (treatment effect) is another description of statistical power.

It is a general rule that in order to increase statistial power to detect the effect of a variable,
you can try to reduce random noise, i.e. exclude subject characteristics which increase
variability. The downside of this is, in extreme you might study populations which
have little to do with those in the real world (for example, many medications
are used by people age 70+, but studies are carried out with subjects aged < 60;
or, comorbidities are excluded, but 80% of subjects with a certain condition
do have comorbidities).

Hope that helps.

Good look to you all in Italy. Hopefully the worst will soon be over for you.

With kind regards

Karabiner
 

Karabiner

TS Contributor
#5
Do you perhaps mean exclusion criteria?
If we stick to comorbidity as an example, then excluding comorbid patients would mean
that factors are excluded which would (probably) not be affected by the treatment. But
these factors can independenttly affect the outcome, e.g. symptom severity. Now the
problem is, every factor independently affecting the outcome will increase variablilty of
the outcome across treatments (since allocation to groups is randomized, comborbidity
will evenly be distributed across groups). This additional variabilty will be independent
of treatment effects. So you will have more "random noise" in your outcome, which
means that difficulty increases to detect the "signal" (the treatment effect). The probabilty
to detect the signal (treatment effect) is another description of statistical power.

As a general rule, in order to increase statistial power to detect the effect of a variable,
you can try to reduce random noise, i.e. exclude subject characteristics which increase
variability. The downside of this is, in extreme you might study populations which
have little to do with those in the real world (for example, many medications
are used by people age 70+, but studies are carried out with subjects aged < 60;
or, comorbidities are excluded, but 80% of subjects with a certain condition
do have comorbidities).

Hope that helps.

Good look to you all in Italy. Hopefully the worst will soon be over for you.

With kind regards

Karabiner
 
#6
Ah! Now I understand! :) I can finally sleep again ;-)
Thank you very much for this very clear answer. It finally makes sense to me.
Greetings,
Chicken