Methodologic and statistical approaches to studying human fertility and environmental exposure.
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.
Type
Journal articleSubject
Data CollectionEnvironmental Exposure
Epidemiologic Studies
Female
Fertility
Fertilization
Humans
Male
Menstruation
Ovulation
Prospective Studies
Research Design
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https://hdl.handle.net/10161/15595Collections
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Show full item recordScholars@Duke
David B. Dunson
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex
data - methods development is directly motivated by challenging applications in ecology/biodiversity,
neuroscience, environmental health, criminal justice/fairness, and more. We seek
to develop new modeling frameworks, algorithms and corresponding code that can be
used routinely by scientists and decision makers. We are also interested in new inference
framework and in studying theoretical properties

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