BACKGROUND: An inverse association between maternal smoking and preeclampsia has been frequently observed in epidemiologic studies for several decades. In the May 2015 issue of this journal, Lisonkova and Joseph described a simulation study suggesting that bias from left truncation might explain the inverse association. The simulations were based on strong assumptions regarding the underlying mechanisms through which bias might occur. METHODS: To examine the sensitivity of the previous authors' conclusions to these assumptions, we constructed a new Monte Carlo simulation using published estimates to frame our data-generating parameters. We estimated the association between smoking and preeclampsia across a range of scenarios that incorporated abnormal placentation and early pregnancy loss. RESULTS: Our results confirmed that the previous authors' findings are highly dependent on assumptions regarding the strength of association between abnormal placentation and preeclampsia. Thus, the bias they described may be less pronounced than was suggested. CONCLUSIONS: Under empirically derived constraints of these critical assumptions, left truncation does not appear to fully explain the inverse association between smoking and preeclampsia. Furthermore, when considering processes in which left truncation may result from the exposure, it is important to precisely describe the target population and parameter of interest before assessing potential bias. We comment on the specification of a meaningful target population when assessing maternal smoking and preeclampsia as a public health issue. We describe considerations for defining a target population in studies of perinatal exposures when those exposures cause competing events (e.g., early pregnancy loss) for primary outcomes of interest.
BACKGROUND: An inverse association between maternal smoking and preeclampsia has been frequently observed in epidemiologic studies for several decades. In the May 2015 issue of this journal, Lisonkova and Joseph described a simulation study suggesting that bias from left truncation might explain the inverse association. The simulations were based on strong assumptions regarding the underlying mechanisms through which bias might occur. METHODS: To examine the sensitivity of the previous authors' conclusions to these assumptions, we constructed a new Monte Carlo simulation using published estimates to frame our data-generating parameters. We estimated the association between smoking and preeclampsia across a range of scenarios that incorporated abnormal placentation and early pregnancy loss. RESULTS: Our results confirmed that the previous authors' findings are highly dependent on assumptions regarding the strength of association between abnormal placentation and preeclampsia. Thus, the bias they described may be less pronounced than was suggested. CONCLUSIONS: Under empirically derived constraints of these critical assumptions, left truncation does not appear to fully explain the inverse association between smoking and preeclampsia. Furthermore, when considering processes in which left truncation may result from the exposure, it is important to precisely describe the target population and parameter of interest before assessing potential bias. We comment on the specification of a meaningful target population when assessing maternal smoking and preeclampsia as a public health issue. We describe considerations for defining a target population in studies of perinatal exposures when those exposures cause competing events (e.g., early pregnancy loss) for primary outcomes of interest.
Authors: Jennifer Weuve; Eric J Tchetgen Tchetgen; M Maria Glymour; Todd L Beck; Neelum T Aggarwal; Robert S Wilson; Denis A Evans; Carlos F Mendes de Leon Journal: Epidemiology Date: 2012-01 Impact factor: 4.822
Authors: Gary M Shaw; Wei Yang; Eric M Roberts; Nima Aghaeepour; Jonathan A Mayo; Kari A Weber; Ivana Maric; Suzan L Carmichael; Virginia D Winn; David K Stevenson; Paul B English Journal: Environ Res Date: 2018-03-31 Impact factor: 6.498