Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure

Hua Fang, Craig Johnson, Nicolas Chevalier, Christian Stopp, Sandra Wiebe, Lauren S. Wakschlag, Kimberly Andrews Espy

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Introduction: Despite efforts to control for confounding variables using stringent sampling plans, selection bias typically exists in observational studies, resulting in unbalanced comparison groups. Ignoring selection bias can result in unreliable or misleading estimates of the causal effect. Methods: Generalized boosted models were used to estimate propensity scores from 42 confounding variables for a sample of 361 neonates. Using emergent neonatal attention and orientation skills as an example developmental outcome, we examined the impact of tobacco exposure with and without accounting for selection bias. Weight at birth, an outcome related to tobacco exposure, also was used to examine the functionality of the propensity score approach. Results: Without inclusion of propensity scores, tobacco-exposed neonates did not differ from their nonexposed peers in attention skills over the first month or in weight at birth. When the propensity score was included as a covariate, exposed infants had marginally lower attention and a slower linear change rate at 4 weeks, with greater quadratic deceleration over the first month. Similarly, exposure-related differences in birth weight emerged when propensity scores were included as a covariate. Conclusions: The propensity score method captured the selection bias intrinsic to this observational study of prenatal tobacco exposure. Selection bias obscured the deleterious impact of tobacco exposure on the development of neonatal attention. The illustrated analytic strategy offers an example to better characterize the impact of prenatal tobacco exposure on important developmental outcomes by directly modeling and statistically accounting for the selection bias from the sampling process.

Original languageEnglish (US)
Pages (from-to)1211-1219
Number of pages9
JournalNicotine and Tobacco Research
Volume12
Issue number12
DOIs
StatePublished - Dec 2010
Externally publishedYes

Fingerprint

Propensity Score
Selection Bias
Tobacco
Birth Weight
Confounding Factors (Epidemiology)
Observational Studies
Newborn Infant
Deceleration

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure. / Fang, Hua; Johnson, Craig; Chevalier, Nicolas; Stopp, Christian; Wiebe, Sandra; Wakschlag, Lauren S.; Espy, Kimberly Andrews.

In: Nicotine and Tobacco Research, Vol. 12, No. 12, 12.2010, p. 1211-1219.

Research output: Contribution to journalArticle

Fang, Hua ; Johnson, Craig ; Chevalier, Nicolas ; Stopp, Christian ; Wiebe, Sandra ; Wakschlag, Lauren S. ; Espy, Kimberly Andrews. / Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure. In: Nicotine and Tobacco Research. 2010 ; Vol. 12, No. 12. pp. 1211-1219.
@article{9b4a0cf147234acfb433659060e5522d,
title = "Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure",
abstract = "Introduction: Despite efforts to control for confounding variables using stringent sampling plans, selection bias typically exists in observational studies, resulting in unbalanced comparison groups. Ignoring selection bias can result in unreliable or misleading estimates of the causal effect. Methods: Generalized boosted models were used to estimate propensity scores from 42 confounding variables for a sample of 361 neonates. Using emergent neonatal attention and orientation skills as an example developmental outcome, we examined the impact of tobacco exposure with and without accounting for selection bias. Weight at birth, an outcome related to tobacco exposure, also was used to examine the functionality of the propensity score approach. Results: Without inclusion of propensity scores, tobacco-exposed neonates did not differ from their nonexposed peers in attention skills over the first month or in weight at birth. When the propensity score was included as a covariate, exposed infants had marginally lower attention and a slower linear change rate at 4 weeks, with greater quadratic deceleration over the first month. Similarly, exposure-related differences in birth weight emerged when propensity scores were included as a covariate. Conclusions: The propensity score method captured the selection bias intrinsic to this observational study of prenatal tobacco exposure. Selection bias obscured the deleterious impact of tobacco exposure on the development of neonatal attention. The illustrated analytic strategy offers an example to better characterize the impact of prenatal tobacco exposure on important developmental outcomes by directly modeling and statistically accounting for the selection bias from the sampling process.",
author = "Hua Fang and Craig Johnson and Nicolas Chevalier and Christian Stopp and Sandra Wiebe and Wakschlag, {Lauren S.} and Espy, {Kimberly Andrews}",
year = "2010",
month = "12",
doi = "10.1093/ntr/ntq170",
language = "English (US)",
volume = "12",
pages = "1211--1219",
journal = "Nicotine and Tobacco Research",
issn = "1462-2203",
publisher = "Oxford University Press",
number = "12",

}

TY - JOUR

T1 - Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure

AU - Fang, Hua

AU - Johnson, Craig

AU - Chevalier, Nicolas

AU - Stopp, Christian

AU - Wiebe, Sandra

AU - Wakschlag, Lauren S.

AU - Espy, Kimberly Andrews

PY - 2010/12

Y1 - 2010/12

N2 - Introduction: Despite efforts to control for confounding variables using stringent sampling plans, selection bias typically exists in observational studies, resulting in unbalanced comparison groups. Ignoring selection bias can result in unreliable or misleading estimates of the causal effect. Methods: Generalized boosted models were used to estimate propensity scores from 42 confounding variables for a sample of 361 neonates. Using emergent neonatal attention and orientation skills as an example developmental outcome, we examined the impact of tobacco exposure with and without accounting for selection bias. Weight at birth, an outcome related to tobacco exposure, also was used to examine the functionality of the propensity score approach. Results: Without inclusion of propensity scores, tobacco-exposed neonates did not differ from their nonexposed peers in attention skills over the first month or in weight at birth. When the propensity score was included as a covariate, exposed infants had marginally lower attention and a slower linear change rate at 4 weeks, with greater quadratic deceleration over the first month. Similarly, exposure-related differences in birth weight emerged when propensity scores were included as a covariate. Conclusions: The propensity score method captured the selection bias intrinsic to this observational study of prenatal tobacco exposure. Selection bias obscured the deleterious impact of tobacco exposure on the development of neonatal attention. The illustrated analytic strategy offers an example to better characterize the impact of prenatal tobacco exposure on important developmental outcomes by directly modeling and statistically accounting for the selection bias from the sampling process.

AB - Introduction: Despite efforts to control for confounding variables using stringent sampling plans, selection bias typically exists in observational studies, resulting in unbalanced comparison groups. Ignoring selection bias can result in unreliable or misleading estimates of the causal effect. Methods: Generalized boosted models were used to estimate propensity scores from 42 confounding variables for a sample of 361 neonates. Using emergent neonatal attention and orientation skills as an example developmental outcome, we examined the impact of tobacco exposure with and without accounting for selection bias. Weight at birth, an outcome related to tobacco exposure, also was used to examine the functionality of the propensity score approach. Results: Without inclusion of propensity scores, tobacco-exposed neonates did not differ from their nonexposed peers in attention skills over the first month or in weight at birth. When the propensity score was included as a covariate, exposed infants had marginally lower attention and a slower linear change rate at 4 weeks, with greater quadratic deceleration over the first month. Similarly, exposure-related differences in birth weight emerged when propensity scores were included as a covariate. Conclusions: The propensity score method captured the selection bias intrinsic to this observational study of prenatal tobacco exposure. Selection bias obscured the deleterious impact of tobacco exposure on the development of neonatal attention. The illustrated analytic strategy offers an example to better characterize the impact of prenatal tobacco exposure on important developmental outcomes by directly modeling and statistically accounting for the selection bias from the sampling process.

UR - http://www.scopus.com/inward/record.url?scp=78649565736&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649565736&partnerID=8YFLogxK

U2 - 10.1093/ntr/ntq170

DO - 10.1093/ntr/ntq170

M3 - Article

C2 - 21030468

AN - SCOPUS:78649565736

VL - 12

SP - 1211

EP - 1219

JO - Nicotine and Tobacco Research

JF - Nicotine and Tobacco Research

SN - 1462-2203

IS - 12

ER -