Healthy Live Births as Censoring Versus Competing Events in Studies of Prenatal Medication Use

Pregnancy loss is recognised as a competing event in studies of prenatal medication use. A healthy live birth also precludes subsequent pregnancy outcomes, yet is often censored in time-to-event analyses.

Using a Monte Carlo simulation, we examined bias resulting from censoring versus accounting for healthy live birth as a competing event in estimates of the total effect of prenatal medication use on pregnancy outcomes.

We simulated 2000 cohorts of 7500 conceptions with chronic hypertension under 12 treatment profiles. Ongoing pregnancies were indexed into the trial and randomly assigned to initiate or not initiate antihypertensives. Using time-to-event methods, we estimated absolute risks, risk differences (RD) per 100 pregnancies, and risk ratios (RR) for two outcomes, mirroring a prior trial: (i) composite fetal death or severe prenatal preeclampsia and (ii) small for gestational age (SGA) live birth. For the composite outcome, we conducted analyses where non-preeclamptic live birth was: (1) a censoring event and (2) a competing event. For SGA live birth, we conducted 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.

For the composite outcome, censoring non-preeclamptic live births overestimated the absolute risk by 42.3 to 49.1 percentage points; RD and RR estimates were biased (e.g., RD bias range -6.18 to 0.46). For SGA live birth, analyses censoring non-SGA live births (with or without fetal death as a competing event) overestimated absolute risk by 30.0 to 37.7 and 40.9 to 52.4 percentage points on average; RD and RR estimates were biased (e.g., RD bias range -7.45 to 0.79 and -9.62 to 1.81, respectively). Analyses in which healthy live births were modelled as competing events produced unbiased risks, RDs and RRs.

Censoring healthy live births resulted in overestimated risks as well as biased and imprecise total treatment effect estimates. Such inaccuracies about risks undermine informed patient-provider decision-making.