Regardless of COVID, it seems that causal inference methods are finally entering the mainsteam.

Use of #DAGs & awareness of #ColliderBias and the #Table2Fallacy are skyrocketting! Even a general medical journal (JAMA) has now produced primers on these issues

But we are still desparately short of advice and guidance on how best to use causal inference methods for applied research; we need more funding for meta-science and methods translation!

#EpiVerse

https://jamanetwork.com/journals/jama/fullarticle/2790247

@[email protected] @[email protected] @[email protected] Abs 2274 cont'd

In the MVA, these factors were a/w disease flare:
>mod/high disease activity
>RTX use
>med holding

🤔Always consider the possibility of #Table2Fallacy & when/how data were collected

#ACR22

@[email protected] @[email protected] Abs 2202 cont'd

Acute care use was higher among those with Black race, who resided in the South, with dual Medicare/Medicaid insurance
🤔Need to consider whether #Table2Fallacy is playing a part in the interpretation of these MVA results

#ACR22

@[email protected] @[email protected] More on #Table2Fallacy here: https://academic.oup.com/aje/article/177/4/292/147738?login=true

#ACR22

@[email protected] @[email protected] #Table2Fallacy

The effect estimates for the blue (exposure of interest) are interpretable ✅

The effect estimates for the red (confounders that were also adjusted for) may not be interpretable 🛑

#ACR22

@[email protected] @[email protected] Finally, #Table2Fallacy

Table 2 is often used to show the adjusted results of the exposure

Variable types are highlighted in this Table 1:
🔹Exposure of interest in blue
🍎Outcomes in red
🍏Confounders (that were adjusted for) in green

#ACR22 https://t.co/Gz7v0Fdu5w