How to address bias and confounding when biological sex is the exposure
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https://uniroma1.zoom.us/j/86881977368?pwd=S WRFcVFjMDZTa0lXZk05TE1zNm5adz09 Passcode: 432940
Ofran Almossawi Great Ormond Street Hospital | University College London Institute of Child Health
Background: Global child mortality rates vary by sex, with higher mortality rates generally found in males. However, previous research has shown that this ratio is reversed in infants admitted to Paediatric Intensive Care Units (PICU). I aimed to determine whether female sex is causally linked to higher mortality in PICU. Methods: I created a longitudinal linked dataset that could be used to evaluate whether sex is causally related to mortality in PICU using routine data on >100,000 children admitted to PICU. I compared a number of estimation methods, namely (i) gcomputation, (ii) propensity score-based singly and doubly robust methods, and (iii) targeted learning aided by machine learning, to determine whether the observed sexratio reversal in PICU mortality is supported by the data. Results: Female biological sex increased the mortality rate in PICU by up to 0.26% (95% CI -0.05%, 0.57%). In the multiply imputed dataset this estimate was 0.35% (95% CI 0.09%, 0.61%). The reversal in mortality rates in PICU was not explained by collider bias. The collider bias was driving the naïve estimate towards the null.
Conclusion: Female biological sex is linked to higher mortality in PICU. Mechanistic reasons underlying this causal relationship are still unknown.
In allegato la locandina con i riferimenti per seguire il seminario online,
Relatore:
Ofran Almossawi
Data:
25/10/2024 - 12:00
Luogo:
[online]
Allegati: