The 'Table 2 fallacy'

 21st December 2022

I was asked a question the other day that got me reflecting on some issues in causal inference.

The question was this: ‘…if a multivariable model includes all the relevant potential confounders then what is the advantage of using causal modelling methods ?’

Having reflected a bit on my own training in causal inference and after a quick Google I found this paper:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3626058/

The ‘Table 2 Fallacy’ refers to the assumption that all the effects of covariates estimated in a multivariable model are independent effect sizes. Indeed, the main exposure of interest is treated like any other covariate within a multivariable model. 

It’s called the ‘Table 2 Fallacy’ because observational studies often use Table 1 to present the descriptives of the data used and Table 2 the results of a multivariable analysis. In my case it should probably be called the ‘Table 3 Fallacy’ as I usually use Table 2 to present my univariable results. 

Interpretation of these covariate effects tends to ignore what is likely to be the true, or at least hypothesised, relationship between the predictor and outcome variables. These are usually expressed in a directed acyclic graph (DAG) prior to an analysis in causal inference. Of course, variables can have other roles beyond confounding such as mediators, moderators, reverse confounders (‘colliders’) etc.

One of the reasons Targeted Learning evades this issue is that it focuses on the main effect of interest. The use of machine learning to generate the ‘propensity scores’ is model agnostic. This means we don’t derive estimates for irrelevant effects. Moreover, without having to estimate various parameters of no direct interest study power (precision) is improved. 

I have to confess, looking back at my previous papers, I’m guilty of the’ Table 2 fallacy’ numerous times. I have recently drafted a paper which involves a multivariable analysis but proceeds to path analysis. Having now reflected on this issue I will include a brief discussion and reference to this issue when presenting my results. 

Wishing you all a Merry Christmas 🎄 and continued happy data modelling in 2023!


Paul

(PI of the RAPPORT project)

PS If I have a New Year’s Resolution for 2023 it will be not to fall for the ‘Table 2 Fallacy’ again…



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