Analysis of repeated pregnancy outcomes

Germaine Buck Louis, Vanja Dukic, Patrick J. Heagerty, Thomas A. Louis, Courtney D. Lynch, Louise M. Ryan, Enrique F. Schisterman, Ann Trumble, Mark Klebanoff, Aiyi Liu, Kai Yu, James Collins, Geary Olsen

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)103-126
Number of pages24
JournalStatistical Methods in Medical Research
Volume15
Issue number2
DOIs
StatePublished - Apr 2006

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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