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Gestational Age at Arrest of Development: An Alternative Approach for Assigning Time at Risk in Studies of Time-Varying Exposures and Miscarriage

Producción científica: Articlerevisión exhaustiva

13 Citas (Scopus)

Resumen

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.

Idioma originalEnglish
Páginas (desde-hasta)570-578
Número de páginas9
PublicaciónAmerican Journal of Epidemiology
Volumen188
N.º3
DOI
EstadoPublished - mar 1 2019

Nota bibliográfica

Publisher Copyright:
© The Author(s) 2019.

Financiación

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.

FinanciadoresNúmero del financiador
Vanderbilt Medical Scientist Training ProgramT32GM07347
National Institute of General Medical Sciences
NIH National Institute of Child Health and Human Development National Center for Medical Rehabilitation ResearchF30HD094345
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 Foundation2579
American Water Works Association Research Foundation
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentR01HD049675, R01HD043883
Eunice Kennedy Shriver National Institute of Child Health and Human Development

    ASJC Scopus subject areas

    • General Medicine

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