Tis is a real group of patients, the estimated median age is real. According to Scklo and Nieto is a simple a and fesible means to establish if effect heterogeinty exsist. My aim is to understand of what kind of interaction i am deling with. Thank you
I understand it is a real group of patients, but the group divided by age isn't a real group; its arbitrary and created by sampling variation. You're not really dealing with a "qualitative interaction" because it's not a qualitative variable, despite the appearance of it.
Frank Harrell Jr. and Stephen Senn are two very well studied and respected biostatisticians (Statisticians by education and practice, so they're pretty well versed in the theory and mathematical nuance behind ideas, in addition to the application). Look up their summaries and work on this problem of "dichotomania" which is incredibly present in and deleterious to biomedical research.
Alternatively, quickly watch this video to give a great deal of explanation and visualization (can probably do 1.5 speed).
Summary: If a med student on rounds said "The patient's sodium is <135, I think we should...", no clinician would say "Great, it's <135, that's all I need to know." They're clearly going to assess risks from the specific serum sodium level, and differently for a patient who is 129 vs 114. This is a good quick analogy that should demonstrate the issue. But, long form is below...or in the video
By categorizing age (or another continuous variable), you are making many assumptions that aren't really true or reasonable and you're devaluing the work you're trying to do.
1) You're assuming that the outcome is relatively homogeneous within groups and relatively heterogeneous between groups (i.e. within each group, the Y values basically fall on a straight,
horizontal line/have the same outcome if categorical). This is clearly not a reasonable assumption. In most biomedical scenarios.
2) You're assuming that continuous variable is not continuously related to the outcome (lines or curves, for example, are ruled out); you assume that the relationship has a discrete jump (think a staircase) to relate Y and that variable (sometimes reasonable in finance, generally not in medicine);
3) the "cut point" or "findings" are not likely replicated in other research.
4) you're assuming that cut point is optimal for every patient, which isn't true.
5) Insurance companies try to use literature to decide what to pay for or what not to pay for (or how much) based on literature, and using arbitrary variables like this can lead to improper policies enacted by those utilizing research. A common issue for this is when people try to relate hospital length of stay to many variables, for example, but the conclusions are spurious and based on improper methodology; in the end, the patient's are at risk of being hurt (I know a few people in public health who have said this has come up in their career).
6) There's a lot more...
I hope this clarifies what is meant by "not a real group" and why a different approach will be more favorable for realistic and repeatable conclusions.