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Methodologic and statistical approaches to studying human fertility and environmental exposure.

dc.contributor.author Tingen, Candace
dc.contributor.author Stanford, Joseph B
dc.contributor.author Dunson, David B
dc.coverage.spatial United States
dc.date.accessioned 2017-10-01T21:16:05Z
dc.date.available 2017-10-01T21:16:05Z
dc.date.issued 2004-01
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/14698936
dc.identifier.issn 0091-6765
dc.identifier.uri https://hdl.handle.net/10161/15595
dc.description.abstract Although there has been growing concern about the effects of environmental exposures on human fertility, standard epidemiologic study designs may not collect sufficient data to identify subtle effects while properly adjusting for confounding. In particular, results from conventional time to pregnancy studies can be driven by the many sources of bias inherent in these studies. By prospectively collecting detailed records of menstrual bleeding, occurrences of intercourse, and a marker of ovulation day in each menstrual cycle, precise information on exposure effects can be obtained, adjusting for many of the primary sources of bias. This article provides an overview of the different types of study designs, focusing on the data required, the practical advantages and disadvantages of each design, and the statistical methods required to take full advantage of the available data. We conclude that detailed prospective studies allowing inferences on day-specific probabilities of conception should be considered as the gold standard for studying the effects of environmental exposures on fertility.
dc.language eng
dc.publisher Environmental Health Perspectives
dc.relation.ispartof Environ Health Perspect
dc.subject Data Collection
dc.subject Environmental Exposure
dc.subject Epidemiologic Studies
dc.subject Female
dc.subject Fertility
dc.subject Fertilization
dc.subject Humans
dc.subject Male
dc.subject Menstruation
dc.subject Ovulation
dc.subject Prospective Studies
dc.subject Research Design
dc.title Methodologic and statistical approaches to studying human fertility and environmental exposure.
dc.type Journal article
duke.contributor.id Dunson, David B|0277221
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/14698936
pubs.begin-page 87
pubs.end-page 93
pubs.issue 1
pubs.organisational-group Duke
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group Statistical Science
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 112


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