Can collider bias fully explain the obesity paradox?
Vivian Viallon, Marine Dufournet

TL;DR
This paper investigates whether collider bias alone can explain the obesity paradox, demonstrating through causal modeling and numerical examples that collider bias may fully account for the observed protective effect of obesity in chronic disease patients.
Contribution
The study clarifies the potential of collider bias to fully explain the obesity paradox using structural causal models and numerical simulations, challenging previous dismissals of collider bias as a sole explanation.
Findings
Collider bias can be much higher than previously reported.
It is possible for collider bias to fully explain the obesity paradox.
Numerical examples show collider bias can produce a negative association despite positive causal effects.
Abstract
The "obesity paradox" has been reported in several observational studies, where obesity was shown to be associated to a decreased mortality in individuals suffering from a chronic disease, such as diabetes or heart failure. Causal arguments have recently been given to explain this apparently paradoxical fact: because the chronic disease is caused by obesity, the observed "protective effect" of obesity among patients with, say, diabetes, actually has no causal value. Recently, Sperrin et al. (2016) relaunched the debate and claimed that the resulting bias, the so-called collider bias, was unlikely to be the main explanation for the obesity paradox. However, a number of issues in their work make their conclusions questionable. In this article, we first study the bias between (i) the association between obesity and early death among patients suffering from the chronic disease …
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Health Systems, Economic Evaluations, Quality of Life · Statistical Methods and Inference
