BACKGROUND

Generally, studies in perinatal epidemiology restrict cohort entry to 20 weeks of gestation, but exposures and outcomes may occur earlier. This restriction may introduce left truncation bias.

OBJECTIVES

To examine the impact of left truncation bias when estimating the causal effect of abruption on perinatal mortality in the context of abnormal placentation, with spontaneous abortion (SAB) as a censoring event.

METHODS

Through 80 Monte Carlo simulation scenarios based on realistic clinical assumptions, we estimated risk differences (RD), risk ratios (RR) and bias parameters for the abruption-perinatal mortality association.

RESULTS

Censoring by SAB ranged from 5.6% to 7.6% across simulation setups. The risk of mortality was underestimated in observable (left-truncated) data at ≥ 20 weeks compared to an unobservable cohort starting follow-up at placental implantation (conception cohort). Underestimation of risks was stronger among abruption pregnancies. RDs for the abruption-mortality association were biased by +1% to +3% among conceptions with normal implantation and by +5% to +43% among abnormal placentation. Due to the disproportionate underestimation of mortality among nonabruption pregnancies, RRs were overestimated by 1.1 to 1.2-fold for normal implantations and by 1.1 to 8.4-fold for abnormal implantations.

CONCLUSIONS

The findings of this simulation study highlight the critical importance of placentation in successful pregnancy. Abnormal placentation has profound consequences for unsuccessful pregnancies, remarkably increasing the risks of early losses, placental abruption and other obstetrical complications. This study underscores that left truncation can bias the abruption-perinatal mortality association, differentially by whether the placentation was normal or abnormal. However, defining the causal question regarding the abruption-perinatal mortality association requires consideration of the target population, which may include all conceptions. In studies of these effects, outcome follow-up capability may introduce left truncation bias. We do not prescribe one analytic approach to account for left truncation, but rather, the approach should be guided by the causal question.