January 2023

Accounting for misclassification and selection bias in estimating effectiveness of self-managed medication abortion

Jayaweera R, Bradshaw P,  Gerdts C, Egwuatu I, Grosso B,  Kristianingrum I, Nmezi S, Zurbriggen R, Ahern J, Moseson H. Epidemiology. Jan 2023. DOI: 10.1097/EDE.0000000000001546

Studies on the effectiveness of self-managed medication abortion may suffer from misclassification and selection bias due to self-reported outcomes and loss of follow-up. Monte Carlo sensitivity analysis can estimate self-managed abortion effectiveness accounting for these potential biases.

We conducted a Monte Carlo sensitivity analysis based on data from the Studying Accompaniment model Feasibility and Effectiveness Study (the SAFE Study), to generate bias-adjusted estimates of the effectiveness of self-managed abortion with accompaniment group support. Between July 2019 and April 2020, we enrolled a total of 1051 callers who contacted accompaniment groups in Argentina and Nigeria for self-managed abortion information; 961 took abortion medications and completed at least one follow-up. Using these data, we calculated measures of effectiveness adjusted for ineligibility, misclassification, and selection bias across 50,000 simulations with bias parameters drawn from pre-specified Beta distributions in R.

After accounting for the potential influence of various sources of bias, bias-adjusted estimates of effectiveness were similar to observed estimates, conditional on chosen bias parameters: 92.68% (95% simulation interval: 87.80%, 95.74%) for mifepristone in combination with misoprostol (versus 93.7% in the observed data) and 98.47% (95% simulation interval: 96.79%, 99.39%) for misoprostol alone (versus 99.3% in the observed data).

After adjustment for multiple potential sources of bias, estimates of self-managed medication abortion effectiveness remain high. Monte Carlo sensitivity analysis may be useful in studies measuring an epidemiologic proportion (i.e., effectiveness, prevalence, cumulative incidence) while accounting for possible selection or misclassification bias.