@misc{10481/80884,
year = {2023},
month = {2},
url = {https://hdl.handle.net/10481/80884},
abstract = {The application of causal mediation analysis (CMA) considering the
mediation effect of a third variable is increasing in epidemiological studies;
however, this requires fitting strong assumptions on confounding bias.
To address this limitation, we propose an extension of CMA combining it
with Mendelian randomization (MRinCMA). We applied the new approach
to analyse the causal effect of obesity and diabetes on pancreatic cancer,
considering each factor as potential mediator. To check the performance of
MRinCMA under several conditions/scenarios, we used it in different
simulated data sets and compared it with structural equation models. For
continuous variables, MRinCMA and structural equation models performed
similarly, suggesting that both approaches are valid to obtain unbiased
estimates. When noncontinuous variables were considered, MRinCMA
presented, overall, lower bias than structural equation models. By applying
MRinCMA, we did not find any evidence of causality of obesity or diabetes
on pancreatic cancer. With this new methodology, researchers would be able
to address CMA hypotheses by appropriately accounting for the confounding
bias assumption regardless of the conditions used in their studies in different
settings.},
organization = {CIBERONC},
organization = {Instituto de Salud Carlos III},
organization = {European Commission},
organization = {Red Tematica de Investigacion Cooperativa en Cancer},
organization = {Instituto de Salud Carlos III},
organization = {Ministry of Science and Innovation, Spain (MICINN)},
organization = {Instituto de Salud Carlos III},
organization = {Spanish Government},
organization = {EU-FP7-HEALTH},
publisher = {Wiley},
keywords = {Causal inference},
keywords = {Causal mediation analysis},
keywords = {Mendelian randomization},
keywords = {Pancreatic cancer},
keywords = {Type 2 diabetes mellitus},
title = {New proposal to address mediation analysis interrogations by using genetic variants as instrumental variables},
doi = {10.1002/gepi.22519},
author = {Coscia, Claudia and Molina Montes, María Ester},
}