Selected Publications
Identification of vaccine effects when exposure status is unknown
Emulation of a target trial with sustained treatment strategies: An application to prostate cancer using both inverse probability weighting and the g-formula
Timing and severity of COVID-19 during pregnancy and the risk of preterm birth in the International Registry of Coronavirus Exposure in Pregnancy
E-values for effect modification and conservative approximations for causal interaction
We provide sensitivity analyses for unmeasured confounding in estimates of effect heterogeneity and causal interaction.
International Registry of Coronavirus Exposure in Pregnancy (IRCEP) — Cohort description and methodological considerations
We describe a pregnancy cohort recruited and followed during the COVID-19 pandemic, as well as some challenges encountered during the process.
Multifoetal gestations mediate the effect of in vitro fertilisation (IVF) on ischaemic placental disease in autologous oocyte IVF more than donor oocyte IVF
Multiple-bias sensitivity analysis using bounds
We show that it is possible to bound the total composite bias due to unmeasured confounding, selection, and differential misclassification, and to use that bound to assess the sensitivity of a risk ratio to any combination of these biases.
Mediating to opportunity: The challenges of translating mediation estimands into policy recommendations
Commenting on an article by Rudolph et al., we describe goals of mediation analysis for informing policy.
Simple sensitivity analysis for control selection bias
We extend a sensitivity analysis approach for selection bias to account for poor control selection in case-control studies.
The importance of mediation in reproductive health studies
We introduce the concept of mediation and provide examples that solidify understanding of mediation for valid discovery and interpretation in the field of reproductive medicine.
Selection mechanisms and their consequences: Understanding and addressing selection bias
Understanding the way in which a study sample relates to the target population is critical for avoiding and addressing bias. Communication about selection bias is aided by the use of causal graphs.
Mediational E-values: Approximate sensitivity analysis for unmeasured mediator-outcome confounding
We show how E-values can be used and easily interpreted for sensitivity analysis in mediation.
Bounding bias due to selection
We provide bounds for selection bias under a variety of situations.