Abstract
The time between arrest of pregnancy development and miscarriage represents a window in which the pregnancy is nonviable and not developing. In effect, the pregnancy loss has already occurred, and additional exposure cannot influence its outcome. However, epidemiologic studies of miscarriage traditionally use gestational age at miscarriage (GAM) to assign time in survival analyses, which overestimates duration of exposure and time at risk. In Right From the Start, a pregnancy cohort study (2000-2012), we characterized the gap between estimated gestational age at arrest of development (GAAD) and miscarriage using transvaginal ultrasound in 500 women recruited from 3 states (North Carolina, Tennessee, and Texas). We compared effect estimates from models using GAAD with GAM to assign time at risk through a simulation study of several exposure patterns with varying effect sizes. The median gap between GAAD and miscarriage was 23 days (interquartile range, 15-32). Use of GAAD decreased the bias and variance of the estimated association for time-varying exposures, whereas half the time using GAM led to estimates that differed from the true effect by more than 20%. Using GAAD to assign time at risk should result in more accurate and consistent characterization of miscarriage risk associated with time-varying exposures.
| Original language | English |
|---|---|
| Pages (from-to) | 570-578 |
| Number of pages | 9 |
| Journal | American Journal of Epidemiology |
| Volume | 188 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 1 2019 |
Bibliographical note
Publisher Copyright:© The Author(s) 2019.
Funding
Author affiliations: Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee (Alexandra C. Sundermann, Sudeshna Mukherjee, Digna R. Velez Edwards, Katherine E. Hartmann); Division of Quantitative Sciences, Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University, Nashville, Tennessee (Alexandra C. Sundermann, Digna R. Velez Edwards, Katherine E. Hartmann); Department of Biostatistics, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee (Pingsheng Wu); and Department of Bioinformatics, Vanderbilt University Medical Center, Nashville, Tennessee (Digna R. Velez Edwards). This work was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grants R01HD043883, R01HD049675, and F30HD094345 to A.C.S.) and the American Water Works Association Research Foundation (grant 2579). Stipend support was provided in part by a National Institute of General Medical Studies award for the Vanderbilt Medical Scientist Training Program (grant T32GM07347 to A.C.S.). Additional infrastructure resources were provided by a Clinical and Translational Science Award (grant UL1TR000445) from the National Center for Advancing Translational Sciences. Conflict of interest: none declared.
| Funders | Funder number |
|---|---|
| Vanderbilt Medical Scientist Training Program | T32GM07347 |
| National Institute of General Medical Sciences | |
| NIH National Institute of Child Health and Human Development National Center for Medical Rehabilitation Research | F30HD094345 |
| NIH National Institute of Child Health and Human Development National Center for Medical Rehabilitation Research | |
| National Center for Advancing Translational Sciences (NCATS) | UL1TR000445 |
| National Center for Advancing Translational Sciences (NCATS) | |
| American Water Works Association Research Foundation | 2579 |
| American Water Works Association Research Foundation | |
| Eunice Kennedy Shriver National Institute of Child Health and Human Development | R01HD049675, R01HD043883 |
| Eunice Kennedy Shriver National Institute of Child Health and Human Development |
Keywords
- bias (epidemiology)
- measurement error
- miscarriage
- pregnancy
- reproductive epidemiology
- simulation study
- spontaneous abortion
ASJC Scopus subject areas
- General Medicine
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