#table2fallacy — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #table2fallacy, aggregated by home.social.
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Just my usual #NightshiftEditor reminder that when you are currently working on the (secondary) analysis of a data set and thinking of applying some regression modelling, here are some good resources:
#STROBE for reporting
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040297Thinking about confounders
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447501/Prediction vs causation
https://academic.oup.com/ije/article/49/6/2074/5831974And avoiding the #Table2Fallacy
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626058/ -
Just my usual #NightshiftEditor reminder that when you are currently working on the (secondary) analysis of a data set and thinking of applying some regression modelling, here are some good resources:
#STROBE for reporting
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040297Thinking about confounders
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447501/Prediction vs causation
https://academic.oup.com/ije/article/49/6/2074/5831974And avoiding the #Table2Fallacy
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626058/ -
Just my usual #NightshiftEditor reminder that when you are currently working on the (secondary) analysis of a data set and thinking of applying some regression modelling, here are some good resources:
#STROBE for reporting
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040297Thinking about confounders
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447501/Prediction vs causation
https://academic.oup.com/ije/article/49/6/2074/5831974And avoiding the #Table2Fallacy
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626058/ -
Reflections on this week's #NightshiftEditor sessions:
1) Suggestions to limit potential misunderstandings when presenting multiple effect estimates
https://academic.oup.com/aje/article/177/4/292/147738
#Table2Fallacy2) From the instant classic "on the 12th day of Christmas, a statistician sent to me":
(i) "Do not dichotomise continuous variables"
(ii) "Carefully account for missing data" #STROBE
https://www.bmj.com/content/379/bmj-2022-072883
3) We all can work on asking better research questions
https://rdcu.be/diEEb -
Reflections on this week's #NightshiftEditor sessions:
1) Suggestions to limit potential misunderstandings when presenting multiple effect estimates
https://academic.oup.com/aje/article/177/4/292/147738
#Table2Fallacy2) From the instant classic "on the 12th day of Christmas, a statistician sent to me":
(i) "Do not dichotomise continuous variables"
(ii) "Carefully account for missing data" #STROBE
https://www.bmj.com/content/379/bmj-2022-072883
3) We all can work on asking better research questions
https://rdcu.be/diEEb -
Reflections on this week's #NightshiftEditor sessions:
1) Suggestions to limit potential misunderstandings when presenting multiple effect estimates
https://academic.oup.com/aje/article/177/4/292/147738
#Table2Fallacy2) From the instant classic "on the 12th day of Christmas, a statistician sent to me":
(i) "Do not dichotomise continuous variables"
(ii) "Carefully account for missing data" #STROBE
https://www.bmj.com/content/379/bmj-2022-072883
3) We all can work on asking better research questions
https://rdcu.be/diEEb -
Reflections on this week's #NightshiftEditor sessions:
1) Suggestions to limit potential misunderstandings when presenting multiple effect estimates
https://academic.oup.com/aje/article/177/4/292/147738
#Table2Fallacy2) From the instant classic "on the 12th day of Christmas, a statistician sent to me":
(i) "Do not dichotomise continuous variables"
(ii) "Carefully account for missing data" #STROBE
https://www.bmj.com/content/379/bmj-2022-072883
3) We all can work on asking better research questions
https://rdcu.be/diEEb -
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!
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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!
-
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!
-
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!
-
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!
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@[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
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@[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 -
@[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 🛑
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@[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