Analysis of repeated pregnancy outcomes

Louis, GB; Dukic, V; Heagerty, PJ; Louis, TA; Lynch, CD; Ryan, LM; Schisterman, EF; Trumble, A; Klebanoff, M; Liu, A; Yu, K; Collins, J; Olsen, G

Analysis of repeated pregnancy outcomes

Louis, GB Dukic, V Heagerty, PJ Louis, TA Lynch, CD Ryan, LM

Schisterman, EF Trumble, A Klebanoff, M Liu, A Yu, K Collins, J Olsen, G

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Publication Type:: Journal Article
Citation:: Statistical Methods in Medical Research, 2006, 15 (2), pp. 103 - 126
Issue Date:: 2006-04-01

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Field	Value	Language
dc.contributor.author	Louis, GB	en_US
dc.contributor.author	Dukic, V	en_US
dc.contributor.author	Heagerty, PJ	en_US
dc.contributor.author	Louis, TA	en_US
dc.contributor.author	Lynch, CD	en_US
dc.contributor.author	Ryan, LM https://orcid.org/0000-0001-5957-2490	en_US
dc.contributor.author	Schisterman, EF	en_US
dc.contributor.author	Trumble, A	en_US
dc.contributor.author	Klebanoff, M	en_US
dc.contributor.author	Liu, A	en_US
dc.contributor.author	Yu, K	en_US
dc.contributor.author	Collins, J	en_US
dc.contributor.author	Olsen, G	en_US
dc.date.issued	2006-04-01	en_US
dc.identifier.citation	Statistical Methods in Medical Research, 2006, 15 (2), pp. 103 - 126	en_US
dc.identifier.issn	0962-2802	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26019
dc.description.abstract	Women tend to repeat reproductive outcomes, with past history of an adverse outcome being associated with an approximate two-fold increase in subsequent risk. These observations support the need for statistical designs and analyses that address this clustering. Failure to do so may mask effects, result in inaccurate variance estimators, produce biased or inefficient estimates of exposure effects. We review and evaluate basic analytic approaches for analysing reproductive outcomes, including ignoring reproductive history, treating it as a covariate or avoiding the clustering problem by analysing only one pregnancy per woman, and contrast these to more modern approaches such as generalized estimating equations with robust standard errors and mixed models with various correlation structures. We illustrate the issues by analysing a sample from the Collaborative Perinatal Project dataset, demonstrating how the statistical model impacts summary statistics and inferences when assessing etiologic determinants of birth weight. © 2006 Edward Arnold (Publishers) Ltd.	en_US
dc.relation.ispartof	Statistical Methods in Medical Research	en_US
dc.relation.isbasedon	10.1191/0962280206sm434oa	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Birth Weight	en_US
dc.subject.mesh	Pregnancy Outcome	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Linear Models	en_US
dc.subject.mesh	Pregnancy	en_US
dc.subject.mesh	Research	en_US
dc.subject.mesh	Databases, Factual	en_US
dc.subject.mesh	Adolescent	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	United States	en_US
dc.subject.mesh	Female	en_US
dc.title	Analysis of repeated pregnancy outcomes	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	15	en_US
utslib.for	010402 Biostatistics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1117 Public Health and Health Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

Women tend to repeat reproductive outcomes, with past history of an adverse outcome being associated with an approximate two-fold increase in subsequent risk. These observations support the need for statistical designs and analyses that address this clustering. Failure to do so may mask effects, result in inaccurate variance estimators, produce biased or inefficient estimates of exposure effects. We review and evaluate basic analytic approaches for analysing reproductive outcomes, including ignoring reproductive history, treating it as a covariate or avoiding the clustering problem by analysing only one pregnancy per woman, and contrast these to more modern approaches such as generalized estimating equations with robust standard errors and mixed models with various correlation structures. We illustrate the issues by analysing a sample from the Collaborative Perinatal Project dataset, demonstrating how the statistical model impacts summary statistics and inferences when assessing etiologic determinants of birth weight. © 2006 Edward Arnold (Publishers) Ltd.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26019