Analysis of repeated pregnancy outcomes.
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.
Louis
Germaine Buck
GB
Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, 6100 Executive Blvd., Room 7B03, Rockville, MD 20852, USA. louisg@mail.nih.gov
Dukic
Vanja
V
Heagerty
Patrick J
PJ
Louis
Thomas A
TA
Lynch
Coutney D
CD
Ryan
Louise M
LM
Schisterman
Enrique F
EF
Trumble
Ann
A
D000293:Adolescent; D000328:Adult; D001724:Birth Weight; D016208:Databases, Factual; D005260:Female; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D011247:Pregnancy; D011256:Pregnancy Outcome; D012106:Research; D014481:United States