Rare Outcomes, Common Treatments: Analytic Strategies Using Propensity Scores

Braitman, Leonard E.; Rosenbaum, Paul R.

doi:10.7326/0003-4819-137-8-200210150-00015

Cited by 341 publications

(248 citation statements)

References 14 publications

(17 reference statements)

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“…We therefore developed propensity score models for more efficient estimation (27,28). Propensity scores were estimated using logistic regression models predicting the probability of using a specific DMARD compared with MTX as a function of all potential confounders listed above (C statistics, a marker for predicting treatment choice, varied between 0.65 and 0.75).…”

Section: Methodsmentioning

confidence: 99%

Anti–tumor necrosis factor α therapy and the risk of serious bacterial infections in elderly patients with rheumatoid arthritis

Schneeweiß¹,

Setoguchi²,

Weinblatt³

et al. 2007

Arthritis & Rheumatism

281

169

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Section: Methodsmentioning

confidence: 99%

Anti–tumor necrosis factor α therapy and the risk of serious bacterial infections in elderly patients with rheumatoid arthritis

Schneeweiß¹,

Setoguchi²,

Weinblatt³

et al. 2007

Arthritis & Rheumatism

281

169

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“…Unmeasured differences between individuals can not be directly addressed in non-randomized designs. However, post-hoc sensitivity analyses can be conducted to assess the magnitude of hidden bias that would need to exist in order to substantively alter findings (Braitman & Rosenbaum, 2002;Leow, Marcus, Zanutto & Boruch, 2004 (Fraser, 2004;Sosin, 2002;El-bassel, Gilbert, Wu, Go & Hill, 2005). A growing interest in the utilization of this method is evident in child welfare studies (Guo, Barth & Gibbons, 2005;DeSena et al, 2005).…”

Section: Propensity Score Matchingmentioning

confidence: 99%

Comparing outcomes for youth in treatment foster care and family-style group care

Lee

Thompson

2008

Children and Youth Services Review

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Group care programs are often criticized for producing poor outcomes, especially in light of community-based alternatives like treatment foster care that have a stronger evidence base. In this study, data from Girls and Boys Town were used to compare outcomes of youth in treatment foster care (n=112) and group care (n=716) using propensity score matching, a method that can minimize selection bias in nonrandomized designs. Eighteen background covariates were used to develop propensity scores for the likelihood of receiving treatment foster care rather than group care. Several matching methods generated balanced samples on which the outcomes were compared. Results found that group care youth were more likely to be favorably discharged, more likely to return home, and less likely to experience subsequent placement in the first six months after discharge. Legal involvement and residing in a home-like environment at follow-up did not differ. Positive outcomes for group care youth suggest that family-style group care programs may promote effectiveness. Keywordstreatment foster care; family-style group care; propensity score matching Prevailing wisdom in systems of care for youth promotes a philosophy of least restrictive and community-based care (Stroul & Friedman, 1986). A growing body of evidence suggests that specialized foster care settings like treatment foster care (Chamberlain & Reid, 1991;Fisher & Chamberlain, 2000) are more effective and cost-efficient than group care (Chamberlain & Reid, 1998;Eddy & Chamberlain, 2000; U.S. Department of Health and Human Services [USDHHS], 1999). In a continuum of care, group care often serves youth whose needs surpass the capacity of traditional family foster care, but are not acute enough to warrant inpatient hospitalization (CWLA, 2004). With the emergence of treatment foster care as a possible alternative to group placements, questions about the continued broad use of group care are increasing (Barth, 2005).Despite widespread questions about youth safety during care (Barth, 2002) and equivocal outcomes following care (Hair, 2005;Wells, 1991), group care programs are prevalent. In this article, the term group care will be used interchangeably with residential care to label programs Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptChild Youth Serv Rev. Author manuscript; available in PMC 2009 January 1. Published in final edited form as:Child Youth Serv Rev. 2008 ; 30(7): 746-757. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript that provide services 24-hours a day to...

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“…Once calculated, the propensity score can be included into statistical models as a single covariate, allowing researchers to statistically balance groups with less complex models and more statistical power (Braitman & Rosenbaum, 2002;Wang & Donnan, 2001). …”

Section: Introductionmentioning

confidence: 99%

Using Propensity Score Modeling to Minimize the Influence of Confounding Risks Related to Prenatal Tobacco Exposure

Fang

Johnson

Chevalier

et al. 2010

Nicotine &Amp; Tobacco Research

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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.

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Rare Outcomes, Common Treatments: Analytic Strategies Using Propensity Scores

Cited by 341 publications

References 14 publications

Anti–tumor necrosis factor α therapy and the risk of serious bacterial infections in elderly patients with rheumatoid arthritis

Anti–tumor necrosis factor α therapy and the risk of serious bacterial infections in elderly patients with rheumatoid arthritis

Comparing outcomes for youth in treatment foster care and family-style group care

Using Propensity Score Modeling to Minimize the Influence of Confounding Risks Related to Prenatal Tobacco Exposure

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