title: Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure.
authors.affiliation: Division of Biostatistics and Health Services Research, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA 01655, USA. hua.fang@umassmed.edu
authors.forename: Hua
authors.initials: H
authors.lastname: Fang
authors.forename: Craig
authors.initials: C
authors.lastname: Johnson
authors.forename: Nicolas
authors.initials: N
authors.lastname: Chevalier
authors.forename: Christian
authors.initials: C
authors.lastname: Stopp
authors.forename: Sandra
authors.initials: S
authors.lastname: Wiebe
authors.forename: Lauren S
authors.initials: LS
authors.lastname: Wakschlag
authors.forename: Kimberly Andrews
authors.initials: KA
authors.lastname: Espy
mesh_terms: D000328:Adult; D015982:Bias; D001724:Birth Weight; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D018730:Infant Behavior; D007231:Infant, Newborn; D011247:Pregnancy; D011256:Pregnancy Outcome / Q000453:epidemiology*; D011297:Prenatal Exposure Delayed Effects; D011446:Prospective Studies; D012907:Smoking / Q000453:epidemiology*; D014028:Tobacco Smoke Pollution / Q000706:statistics & numerical data*; D055815:Young Adult
chemical_list: D014028:Tobacco Smoke Pollution