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Does Adjusting for Causal Intermediate Confounders Resolve the Perinatal Crossover Paradox?
Wen Wei Loh1, Cande V Ananth2,3,4,5,6
1From the Department of Methodology and Statistics, Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, Maastricht, The Netherlands.
Background:
Mediation analyses of the preeclampsia-perinatal outcome association through preterm birth (PTB) have produced paradoxical findings. For example, preeclamptic births at preterm gestations show a lower risk of adverse outcomes than normotensive births. These results have been explained by unmeasured baseline confounding between PTB and outcomes, with PTB as the sole mediator. However, other intermediate variables, such as placental abruption, small for gestational age (SGA) births, and chorioamnionitis, are confounders yet are excluded because they occur after preeclampsia.
Methods:
Using data from the Consortium on Safe Labor (2002-2008; ), we utilized interventional indirect effects to examine whether adjusting for causal intermediates mitigates confounding bias to resolve the perinatal paradox. We compared two approaches to handle intermediate confounding by abruption, SGA, and chorioamnionitis when PTB is the focal mediator: as exposure-induced confounders or as multiple mediators. We developed bias formulas to assess unmeasured confounding for interventional effects.
Results:
When PTB was the sole mediator, the estimated protective direct effect of preeclampsia (risk ratio = 0.60; 95% confidence interval = 0.52, 0.71) was in line with previous paradoxical findings. The estimated protective effect persisted even after adjusting for intermediate confounders. Sensitivity analyses suggested an unmeasured confounder must strongly influence the outcome to resolve the paradox.
Conclusion:
Adjusting for causal intermediates such as abruption, SGA, and chorioamnionitis is inadequate to eliminate unmeasured PTB-perinatal mortality confounding. The paradox of preeclampsia's protective direct effect on mortality remains unresolved. Sensitivity analyses to unmeasured confounding are effective in bolstering conclusions from causal mediation analyses and should be more widely applied.
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