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

dc.contributor.author

Tingen, Candace

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Stanford, Joseph B

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Dunson, David B

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United States

dc.date.accessioned

2017-10-01T21:16:05Z

dc.date.available

2017-10-01T21:16:05Z

dc.date.issued

2004-01

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.identifier

https://www.ncbi.nlm.nih.gov/pubmed/14698936

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0091-6765

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https://hdl.handle.net/10161/15595

dc.language

eng

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Environmental Health Perspectives

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Environ Health Perspect

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Data Collection

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Environmental Exposure

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Epidemiologic Studies

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Female

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Fertility

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Fertilization

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Humans

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Male

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Menstruation

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Ovulation

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Prospective Studies

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Research Design

dc.title

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

dc.type

Journal article

pubs.author-url

https://www.ncbi.nlm.nih.gov/pubmed/14698936

pubs.begin-page

87

pubs.end-page

93

pubs.issue

1

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Duke

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Duke Institute for Brain Sciences

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Electrical and Computer Engineering

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Institutes and Provost's Academic Units

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Pratt School of Engineering

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Statistical Science

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Trinity College of Arts & Sciences

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University Institutes and Centers

pubs.publication-status

Published

pubs.volume

112

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