Several epidemiologic designs allow studying fecundability, the monthly probability of pregnancy occurrence in noncontracepting couples in the general population. These designs may, to varying extents, suffer from attenuation bias and other biases. We aimed to compare the main designs: incident and prevalent cohorts, pregnancy-based, and current duration approaches.
A realistic simulation model produced individual reproductive lives of a fictitious population. We drew random population samples according to each study design, from which the cumulative probability of pregnancy was estimated. We compared the abilities of the designs to highlight the impact of an environmental factor influencing fecundability, relying on the Cox model with censoring after 12 or 6 months.
Regarding the estimation of the cumulative probability of pregnancy, the pregnancy-based approach was the most prone to bias. When we considered a hypothetical factor associated with a hazard ratio (HR) of pregnancy of 0.7, the estimated HR was in the 0.78–0.85 range, according to designs. This attenuation bias was largest for the prevalent cohort and smallest for the current duration approach, which had the largest variance. The bias could be limited in all designs by censoring durations at 6 months.
Attenuation bias in HRs cannot be ignored in fecundability studies. Focusing on the effect of exposures during the first 6 months of unprotected intercourse through censoring removes part of this bias. For risk factors that can accurately be assessed retrospectively, retrospective fecundity designs, although biased, are not much more strongly so than logistically more intensive designs entailing follow-up.
From the aJulius Center for Health Sciences and Primary Care, Department of Biostatistics and Research Support, University Medical Center, Utrecht, The Netherlands
bDepartment of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
cINED (French Institute for Demographic Studies) and French Academy of Sciences, Paris, France
dDepartment of Biostatistics, University of Copenhagen, Copenhagen, Denmark
eTeam of Environmental Epidemiology Applied to Reproduction and Respiratory Health, U1209, Inserm, CNRS and University Grenoble-Alpes Joint Research Center (IAB), Grenoble, France.
Submitted December 25, 2017; accepted September 3, 2018.
The authors report no conflicts of interest.
The computing code can be obtained by request to the first author.
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Correspondence: Rémy Slama, Inserm, IAB Research Center, Team of Environmental Epidemiology, Allée des Alpes, Site Santé, 38700 La Tronche, France. E-mail: Remy.firstname.lastname@example.org.