Evidence map›Paper›PMID 26086447›Full record

ArticleJournal of public health dentistry2016

Propensity score weighting: an application to an Early Head Start dental study.

Jacqueline M Burgette, John S Preisser, R Gary Rozier

Abstract read
In one paragraph

Article in Journal of public health dentistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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  16. Enrollment in early head start and oral health-related quality of life.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jacqueline M BurgetteDepartment of Pediatric Dentistry, School of Dentistry, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
John S PreisserDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
R Gary RozierDepartment of Health Policy and Management, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

Prevention of Dental Caries in Early Head Start ChildrenR01DE018236 · NIDCR · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ROZIER, RICHARD GARY · 2008 to 2012
$4.9M
Clinical Research Training in Oral Diseases for Future *T32DE017245 · NIDCR · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BECK, JAMES D · 2005 to 2010
$2.2M
NIDCR NIH HHS R01 DE018236NIDCR NIH HHS T32 DE017245
6 · The paper itself

Abstract

objectivesNonrandomized group assignment in intervention studies can lead to imbalances in preintervention covariates and biased effect estimates. We use propensity score estimation to account for such imbalances in an Early Head Start (EHS) dataset with rich pretreatment information. We compare propensity score results using standard logistic regression models (LRMs) versus generalized boosted models (GBMs).

methodsWe estimated propensity scores using 47 socio-demographic characteristics and EHS enrollment criteria obtained by parent interviews from a state-wide sample of 637 EHS and 930 Medicaid-matched control children. LRMs and GBMs were used to estimate propensity scores related to EHS enrollment. Performance of both approaches was evaluated via a) measures of balance of pretreatment covariate distributions between treated and control subjects, and b) stability of propensity score weights measured by the effective sample size.

resultsDistributions of all variables were balanced for EHS and non-EHS groups using propensity score weights calculated with LRM and GBM. Compared with LRM, GBM resulted in better balance between treated and propensity score-weighted control distributions. The effective sample size of the controls decreased from 930 subjects to 507 with GBM and to 335 with LRM.

conclusionAlthough propensity scores derived from GBM and LRM both effectively balanced observed preintervention covariates, GBM resulted in better covariate balance compared with LRM. GBM also resulted in a larger effective sample size of the control group compared with LRM. Propensity score weighting using GBM is an effective statistical method to reduce confounding due to imbalanced distributions of measured preintervention covariates in this EHS intervention study.

Indexed as

Early Medical InterventionPropensity ScoreAlgorithmsDental Care for ChildrenFemaleHealth Status IndicatorsHumansInfantInterviews as TopicLogistic ModelsMaleNorth Carolinadental care for childrenEarly Head Starthealth services researchnonparametric statistics

Identifiers

PMID26086447
PMCPMC4684474

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.