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Analysis of repeated pregnancy outcomesEpidemiology Branch Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, Rockville, MD, USA, louisg{at}mail.nih.gov
Department of Health Studies, University of Chicago, 5841 S. Maryland Ave., MC 2007, Chicago, IL 60637, USA
Department of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195, USA
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205-2179, USA
Epidemiology Branch Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, Rockville, MD, USA
Department of Biostatistics, Harvard School of Public Health, Harvard University, 655 Huntington Ave., Boston, MA 02115-6009, USA
Epidemiology Branch Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, Rockville, MD, USA
Epidemiology Branch Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, Rockville, MD, USA Pregnancy Modeling Working Group 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.
Statistical Methods in Medical Research, Vol. 15, No. 2,
103-126 (2006) This article has been cited by other articles:
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