TitleThe Curse of the Perinatal Epidemiologist: Inferring Causation Amidst Selection
Publication TypeJournal Article
Year of Publication2018
AuthorsSnowden, JM, Bovbjerg, ML, Dissanayake, M, Basso, O
JournalCurrent Epidemiology Reports
Volume5
Issue19
Date Published09/2018
Abstract
 

Purpose of Review

Human reproduction is a common process and one that unfolds over a relatively short time, but pregnancy and birth processes are challenging to study. Selection occurs at every step of this process (e.g., infertility, early pregnancy loss, and stillbirth), adding substantial bias to estimated exposure-outcome associations. Here, we focus on selection in perinatal epidemiology, specifically, how it affects research question formulation, feasible study designs, and interpretation of results.

Recent Findings

Approaches have recently been proposed to address selection issues in perinatal epidemiology. One such approach is the ongoing pregnancies denominator for gestation-stratified analyses of infant outcomes. Similarly, bias resulting from left truncation has recently been termed “live birth bias,” and a proposed solution is to control for common causes of selection variables (e.g., fecundity, fetal loss) and birth outcomes. However, these approaches have theoretical shortcomings, conflicting with the foundational epidemiologic concept of populations at risk for a given outcome.

Summary

We engage with epidemiologic theory and employ thought experiments to demonstrate the problems of using denominators that include units not “at risk” of the outcome. Fundamental (and commonsense) concerns of outcome definition and analysis (e.g., ensuring that all study participants are at risk for the outcome) should take precedence in formulating questions and analysis approaches, as should choosing questions that stakeholders care about. Selection and resulting biases in human reproductive processes complicate estimation of unbiased causal exposure-outcome associations, but we should not focus solely (or even mostly) on minimizing such biases.

URLhttps://link.springer.com/article/10.1007%2Fs40471-018-0172-x
DOI10.1007/s40471-018-0172-x
Short TitleCurr Epidemiol Rep