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Healthy Live Births Should be Considered as Competing Events when Estimating the Total Effect of Prenatal Medication Use on Pregnancy Outcomes

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arxiv 2410.23521 v1 pith:YPJCI4WO submitted 2024-10-31 stat.AP

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keywords livecompetingbirtheventhealthyoutcomespregnancyprenatal
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Pregnancy loss is recognized as an important competing event in studies of prenatal medication use. However, a healthy live birth also precludes subsequent adverse pregnancy outcomes, yet these events are often censored. Using Monte Carlo simulation, we examine bias that results from failure to account for healthy live birth as a competing event in estimates of the total effect of prenatal medication use on pregnancy outcomes. We simulated data for 12 trials estimating the effect of antihypertensive initiation versus non-initiation on two outcomes: (1) composite fetal death or severe prenatal preeclampsia and (2) small-for-gestational-age (SGA) live birth. We used time-to-event methods to estimate absolute risks, risk differences and risk ratios. For the composite outcome, we conducted two analyses where non-preeclamptic live birth was (1) a censoring event and (2) a competing event. For SGA live birth, we conducted three analyses where fetal death and non-SGA live birth were (1) censoring events, (2) a competing event and censoring event, respectively; and (3) competing events. In all analyses, censoring healthy live births led to inflated absolute risk estimates as well as bias and imprecise treatment effect estimates. Studies of prenatal exposures on pregnancy outcomes should analyze healthy live births as competing risks to estimate unbiased total treatment effects.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bias in studies of prenatal exposures using real-world data due to pregnancy identification method

    stat.AP 2025-04 conditional novelty 6.0 of 10

    Pregnancy cohorts built from observed deliveries or outcomes are biased when outcomes are missing, while cohorts including all pregnancies are unbiased only when missingness is due to measured covariates.

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