Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure.
Fang
Hua
H
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
Johnson
Craig
C
Chevalier
Nicolas
N
Stopp
Christian
C
Wiebe
Sandra
S
Wakschlag
Lauren S
LS
Espy
Kimberly Andrews
KA
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
D014028:Tobacco Smoke Pollution