title: Maternal Weight Gain During Pregnancy: Comparing Methods to Address Bias Due to Length of Gestation in Epidemiological Studies.
abstract: BACKGROUND
  Studies examining total gestational weight gain (GWG) and outcomes associated with gestational age (GA) are potentially biased. The z-score has been proposed to mitigate this bias. We evaluated a regression-based adjustment for GA to remove the correlation between GWG and GA, and compared it to published weight-gain-for-gestational-age z-scores when applied to a study sample with different underlying population characteristics.
  
  
  METHODS
  Using 65 643 singleton deliveries to normal weight women at 12 US clinical sites, we simulated a null association between GWG and neonatal mortality. Logistic regression was used to estimate approximate relative risks (RR) of neonatal mortality associated with GWG, unadjusted and adjusted for GA, and the z-score, overall and within study sites. Average RRs across 5000 replicates were calculated with 95% coverage probability to indicate model bias and precision, where 95% is nominal.
  
  
  RESULTS
  Under a simulated null association, total GWG resulted in a biased mortality estimate (RR = 0.87; coverage = 0%); estimates adjusted for GA were unbiased (RR = 1.00; coverage = 94%). Quintile-specific RRs ranged from 0.97-1.03. Similar results were observed for site-specific analyses. The overall z-score RR was 0.97 (84% coverage) with quintile-specific RRs ranging from 0.64-0.90. Estimates were close to 1.0 at most sites, with coverage from 70-94%. Sites 1 and 6 were biased with RRs of 0.66 and 1.43, respectively, and coverage of 70% and 80%.
  
  
  CONCLUSIONS
  Adjusting for GA achieves unbiased estimates of the association between total GWG and neonatal mortality, providing an accessible alternative to the weight-gain-for-gestational-age z-scores without requiring assumptions concerning underlying population characteristics.
authors.affiliation: Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD.
authors.forename: Stefanie N
authors.identifier: http://orcid.org/0000-0003-4312-708X
authors.initials: SN
authors.lastname: Hinkle
authors.affiliation: Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD.
authors.forename: Emily M
authors.initials: EM
authors.lastname: Mitchell
authors.affiliation: Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD.
authors.forename: Katherine L
authors.initials: KL
authors.lastname: Grantz
authors.affiliation: Glotech, Inc, Rockville, MD.
authors.forename: Aijun
authors.initials: A
authors.lastname: Ye
authors.affiliation: Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD.
authors.forename: Enrique F
authors.initials: EF
authors.lastname: Schisterman
mesh_terms: D000328:Adult; D015982:Bias; D005260:Female; D005865:Gestational Age; D006801:Humans; D016015:Logistic Models; D009035:Mothers; D011247:Pregnancy; D011248:Pregnancy Complications / Q000453:epidemiology; D011256:Pregnancy Outcome; D012307:Risk Factors; D014481:United States / Q000453:epidemiology; D015430:Weight Gain
keywords: Bias; Epidemiology; Gestational age; Pregnancy; Weight gain