Methods of confounder selection in obstetrics and gynaecology studies: An overview of recent practice

Identifying confounders and selecting variables to include when controlling for confounding ('adjustment sets') are key methodological decisions in studies that estimate the effect of an intervention or exposure, and relevant reporting guidelines suggest including the justification for how confounders were selected (Strengthening the Reporting of Observational Studies in Epidemiology, STROBE, item 16a).1 Excluding confounders or including variables that are not confounders in adjustment sets can lead to spurious findings. For example, because pre-eclampsia influences gestational age at birth, adjusting for gestational age can make pre-eclampsia appear protective against cerebral palsy.2 Contemporary approaches to selecting adjustment sets use content knowledge about the known or likely causal relationships between potential confounders and the exposure and outcome (e.g. using a directed acyclic graph, DAG).2, 3 At the same time, data-driven methods for identifying adjustment sets (e.g. selecting variables that are significantly associated with the outcome) continue to be used despite these methods not being able to reliably identify confounders.4 Our objective was to describe the prevalence of different approaches of selecting adjustment sets in a recent sample of non-randomised studies in obstetrics and gynaecology. We reviewed all full-length original research articles from January 2022 through June 2022 in three obstetrics and gynaecology journals (American Journal of Obstetrics and Gynaecology, British Journal of Obstetrics and Gynaecology and Obstetrics & Gynaecology). We included non-randomised studies investigating the relationship between an intervention/exposure and a health outcome. We excluded descriptive studies (e.g. describing trends), predictive studies (e.g. identifying high-risk patients) and studies where the aim was unclear. We classified studies as using a data-driven method if the final adjustment set was determined in whole or in part using data-driven methods, and categorised these methods into significance testing (e.g. selecting variables associated with the outcome) and change-in-estimate approaches (e.g. selecting variables that, when included, changed the estimate of interest by 10%). We classified studies as using content knowledge if the authors reported specifying confounders a priori or discussed the relationship between at least one potential confounder and the exposure and outcome. Of the 252 studies published during our study period, 129 met inclusion criteria. We excluded 29 descriptive or predictive studies, eight studies with unclear aims (e.g. 'risk factor' studies) and 86 studies with other aims or designs (e.g. trials, validation studies). The results are summarised in Table 1. Almost half of the included studies (44%) did not explicitly state what approach was used to identify their adjustment sets. Of the 72 studies that were more explicit in their justification, 39% used data-driven methods to select some or all variables in the adjustment set. Of the 44 studies that used content knowledge, 16% used a DAG. We found that justifications for the selection of adjustment sets were often unclear and the use of data-driven methods to select adjustment sets was common in recent studies in obstetrics and gynaecology. Data-driven methods for selecting adjustment sets can cause bias by excluding confounders or including factors caused by the exposure or outcome, but can be useful for variable selection in predictive models.2-4 The persistent use of these methods for causal questions may result from a combination of a lack of awareness of the potential for bias and a lack of clarity on the distinctions between descriptive, predictive and causal questions.5 Content knowledge is required to select adjustment sets and DAGs offer a compelling framework for clearly reporting underlying modelling assumptions (for examples from obstetrics and gynaecology, see Ananth and Schisterman).2 For studies that consider a large number of potential confounders, a more pragmatic approach than presenting a single DAG is to simply include all variables that are (or are proxies for) common causes of the exposure and outcome (for detailed criteria, see Vanderweele).3 PMS conceived of the study, with input on design from JAH and SH. PMS reviewed the studies. All authors interpreted the results. PMS wrote the first draft of the manuscript, with revisions from JAH and SH. All authors approved the final manuscript. None. None. None. The data that support the findings of this study are available from the corresponding author upon reasonable request.

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