Advanced Causal Inference Methods in Obstetrics and Gynecology: A Simulation Study on Preeclampsia Prevention
Shi, W.
Show abstract
AO_SCPLOWBSTRACTC_SCPLOWPreeclampsia remains a leading cause of maternal and perinatal morbidity and mortality world-wide. Early preventive interventions, including low-dose aspirin therapy, reduce risk in high-risk pregnancies. Observational studies often face confounding, nonlinear covariate interactions, and heterogeneous treatment effects. We conducted a comprehensive simulation study comparing naive logistic regression (NL), propensity score weighting (PSW), regression adjustment (RA), doubly robust estimation (DR), targeted maximum likelihood estimation (TMLE), and causal forests (CF) in estimating aspirin effects on preeclampsia outcomes. Simulated cohorts reflected diverse confounding structures and treatment effect heterogeneity. Performance metrics included bias, standard deviation (SD), root mean squared error (RMSE), and coverage probability. TMLE and CF consistently yielded unbiased estimates and identified subgroup-specific effects, highlighting their potential in observational OB/GYN research.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Causal modeling of chronic kidney disease in a participatory framework for informing the inclusion of social drivers in health algorithms 92%
- Learning from local to global - an efficient distributed algorithm for modeling time-to-event data 91%
- Learning Decision Thresholds for Risk-Stratification Models from Aggregate Clinician Behavior 91%
Similar papers in this journal
- PALM: Patient-centered Treatment Ranking via Large-scale Multivariate Network Meta-analysis 93%
- Retrospective varying coefficient association analysis of longitudinal binary traits: application to the identification of genetic loci associated with hypertension 91%
- MASH: Mediation Analysis of Survival Outcome and High-dimensional Omics Mediators with Application to Complex Diseases 91%
Similar papers in this journal
- Utility of polygenic embryo screening for disease depends on the selection strategy 91%
- Unequal Recovery in Colorectal Cancer Screening Following the COVID-19 Pandemic: A Comparative Microsimulation Analysis 90%
- Sparse Dimensionality Reduction Approaches in Mendelian Randomization with highly correlated exposures 90%
Similar papers in this journal
- Improving Pre-eclampsia Risk Prediction by Modeling Individualized Pregnancy Trajectories Derived from Routinely Collected Electronic Medical Record Data 91%
- Zero-shot Interpretable Phenotyping of Postpartum Hemorrhage Using Large Language Models 91%
- Continuous-Time and Dynamic Suicide Attempt Risk Prediction with Neural Ordinary Differential Equations 90%
Similar papers in this journal
- Widely accessible prognostication using medical history for fetal growth restriction and small for gestational age in nationwide insured women 94%
- Can machine learning improve risk prediction of incident hypertension? An internal method comparison and external validation of the Framingham risk model using HUNT Study data 91%
- Better individual-level risk models can improve the targeting and life-saving potential of early-mortality interventions 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.