Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure.
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.Method...
| Publicado en: | Nicotine & Tobacco Research Vol. 12; no. 12; pp. 1211 - 1220 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Dec2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104952938&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104952938 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14622203 57O jtl: Nicotine & Tobacco Research issn: 14622203 maglogo: N pubinfo: dt: Dec2010 vid: 12 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104952938 NLM21030468 2010867579 10.1093/ntr/ntq170 NLM21030468 PMC2991623 104952938 ppf: 1211 ppct: 9 formats: tig: atl: Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure. aug: au: Fang H Johnson C Chevalier N Stopp C Wiebe S Wakschlag LS Espy KA Fang, Hua Johnson, Craig Chevalier, Nicolas Stopp, Christian Wiebe, Sandra Wakschlag, Lauren S Espy, Kimberly Andrews affil: Division of Biostatistics and Health Services Research, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA 01655, USA sug: subj: Data Analysis, Statistical Pregnancy Outcomes Prenatal Exposure Delayed Effects Smoking Epidemiology Passive Smoking Adult Bias (Research) Birth Weight Prospective Studies Female Human Infant Behavior Infant, Newborn Pregnancy Young Adult Adult: 19-44 years Infant, Newborn: birth-1 month Female 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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