This is the continuation of a debated that started in another Forum post about categorization of diseases in a population.
What is Table 1? That is actually not an easy question. The various guidelines STROBE, CONSORT and TREND state, more or less in unison, that it should contain “baseline demographic and clinical characteristics” of participants in each study and “information on potential confounders”. (It also mentions exposure, missing data and follow-up time, but that is outside this discussion).
There even is, or was, a Table 1 Project at Duke.
So, let’s talk about “clinical characteristics”. This could be one of two things:
- Certain specific diseases that are relevant to the question, including the confounders.
- General categories summarizing co-morbidities or medical history.
@Patrick_Ryan’s list, which is identical to the list of the Legend paper, seems to be a mixture of both: There are very specific conditions such as COPD, and generic categories like “Malignant neoplastic disease”. Most of them are somewhere between.
I would claim the more general or category-like the conditions are, the more you could use a mechanism like the summary script discussed in that other Forum discussion. The more fine-grained they are the more it this is the domain of @Gowtham_Rao’s phenotypes. A library of these seems a good thing, so that wheels don’t get reinvented all the time.
However, I am a little uneasy about two aspects of that:
How do we define “relevant” conditions? Studies, including Legend, do not explain how they come to that choice. Why, for example, are “Malignant neoplasm of anorectum” or “Primary malignant neoplasm of prostate” relevant to antihypertensive drugs, but skin and kidney cancer, which are more common, are not? Usually, the OHDSI ideology tells us to stay away from hand-picked expert choices and recommends a systematic approach. Technically, it would be easy to report on thousands of conditions, but who is going to phenotype them all, and how should they be reported meaningfully? The Charybdis study solved that problem by reporting simply on Condition concepts, dumping incidence numbers in the thousands.
How can we standardize conditions if they are specific to a study? Is that Patrick list going to work for all studies? In, say, ophthalmology, obstetrics or immunology?
And then they are the “potential confounders”? Do we have an idea how to tackle that?