Propensity score weighting: an application to an E arly H ead S tart dental study
Open accessAbstract
Objectives: Nonrandomized 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 E arly H ead S tart (EHS) dataset with rich pretreatment information. We compare propensity score results using standard logistic regression models (LRM s) versus generalized boosted models (GBM s). Methods: We 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.LRM s and GBM s 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. Results: Distributions 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. Conclusion: Although 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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Four baseline covariates were included because of their potential impact on all four dependent variables: parent health literacy, parent overall health, survey language, and a propensity score derived from 47 socio-demographic characteristics and Early Head Start enrollment criteria.
“We included four baseline covariates in the analyses because of their potential impact on all four dependent variables: parent health literacy, parent overall health, survey language and a generalized boosted model propensity score derived from 47 socio-demographic characteristics and EHS enrollment criteria.20”
From Access to preventive services after the integration of oral health care into early childhood education and medical care (2018) · cites this paper as [20]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Statistical Power Analysis for the Behavioral Sciences
Cohen, J. (2013) · doi:10.4324/9780203771587 · Cited by 25,373 · see citing works
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653 · see citing works
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884 · see citing works
Rosenbaum, P., & Rubin, D. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,854 · see citing works
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,411 · see citing works
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
Hirano, K., Imbens, G. W., & Ridder, G. (2003) · Econometrica · doi:10.1111/1468-0262.00442 · Cited by 2,525 · see citing works
McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.4.403 · Cited by 1,317 · see citing works
Rosenbaum, P., & Rubin, D. (1983) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1983.tb01242.x · Cited by 1,264 · see citing works
Hirano, K., & Imbens, G. W. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020371312283 · Cited by 1,039 · see citing works
Improving propensity score weighting using machine learning
Lee, B. K., Lessler, J., & Stuart, E. A. (2009) · Statistics in Medicine · doi:10.1002/sim.3782 · Cited by 888 · see citing works
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010) · Psychological Methods · doi:10.1037/a0019623 · Cited by 802 · see citing works
Average causal effects from nonrandomized studies: A practical guide and simulated example.
Schafer, J. L., & Kang, J. (2008) · Psychological Methods · doi:10.1037/a0014268 · Cited by 529 · see citing works
Weight Trimming and Propensity Score Weighting
Lee, B. K., Lessler, J., & Stuart, E. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018174 · Cited by 507 · see citing works
Effects of Misspecification of the Propensity Score on Estimators of Treatment Effect
Drake, C. (1993) · Biometrics · doi:10.2307/2532266 · Cited by 347 · see citing works
Housing Affordability and Health Among Homeowners and Renters
Pollack, C. E., Griffin, B. A., & Lynch, J. F. (2010) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2010.08.002 · Cited by 303 · see citing works
Combining propensity score matching and group-based trajectory analysis in an observational study.
Haviland, A. M., Nagin, D., & Rosenbaum, P. (2007) · Psychological methods · doi:10.1037/1082-989x.12.3.247 · Cited by 277 · see citing works
Toolkit for Weighting and Analysis of Nonequivalent Groups: A tutorial for the twang package
Ridgeway, G. K., McCarey, D., Morral, A. R., Burgette, L. F., & Grin, B. A. (2014) · Cited by 274 · see citing works
Effects of Park Improvements on Park Use and Physical Activity
Cohen, D. A., Golinelli, D., Williamson, S. L., Sehgal, A., Marsh, T., & McKenzie, T. L. (2009) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2009.07.017 · Cited by 173 · see citing works
The Relationship Between Methamphetamine Use and Increased Dental Disease
Shetty, V., Mooney, L. J., Zigler, C. M., Belin, T. R., Murphy, D. A., & Rawson, R. A. (2010) · The Journal of the American Dental Association · doi:10.14219/jada.archive.2010.0165 · Cited by 149 · see citing works
Drawing Causal Inferences Using Propensity Scores: A Practical Guide for Community Psychologists
Lanza, S. T., Moore, J. E., & Butera, N. M. (2013) · American Journal of Community Psychology · doi:10.1007/s10464-013-9604-4 · Cited by 115 · see citing works
Squeezing the Balloon: Propensity Scores and Unmeasured Covariate Balance
Brooks, J. M., & Ohsfeldt, R. L. (2012) · Health Services Research · doi:10.1111/1475-6773.12020 · Cited by 111 · see citing works
Cost-effectiveness of Preventive Oral Health Care in Medical Offices for Young Medicaid Enrollees
Stearns, S. C., Rozier, R. G., Kranz, A. M., Pahel, B. T., & Quiñonez, R. B. (2012) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpediatrics.2012.797 · Cited by 76 · see citing works
Self-citationToh, S., Gagne, J. J., Rassen, J. A., FIREMAN, B. H., Kulldorff, M., & Brown, J. S. (2013) · Medical Care · doi:10.1097/mlr.0b013e31829b1bb1 · Cited by 65 · see citing works
Periodontal Disease and Recurrent Vascular Events in Stroke/Transient Ischemic Attack Patients
Sen, S., Sumner, R., Hardin, J. W., Barros, S. P., MOSS, K. L., Beck, J. D., & Offenbacher, S. (2013) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2013.06.024 · Cited by 51 · see citing works
Total tooth loss and complete denture use in older adults with intellectual disabilities in I reland
Phadraig, C. M. G., McCallion, P., Cleary, E., McGlinchey, E., Burke, E. A., McCarron, M., & Nunn, J. (2014) · Journal of Public Health Dentistry · doi:10.1111/jphd.12077 · Cited by 46 · see citing works
Karwa, V., Slavković, A., & Donnell, E. T. (2011) · The Annals of Applied Statistics · doi:10.1214/10-aoas440 · Cited by 45 · see citing works
Chaudhari, M., Hubbard, R. A., Reid, R. J., et al. (2012) · BMC Oral Health · doi:10.1186/1472-6831-12-20 · Cited by 37 · see citing works
Preventive Services by Medical and Dental Providers and Treatment Outcomes
Kranz, A. M., Rozier, R. G., Preisser, J. S., Stearns, S. C., Weinberger, M., & Lee, J. Y. (2014) · Journal of Dental Research · doi:10.1177/0022034514536731 · Cited by 25 · see citing works
Self-citationMurphy, D. A., Harrell, L., Fintzy, R., Belin, T. R., Gutierrez, A., Vitero, S., & Shetty, V. (2014) · The Journal of Behavioral Health Services & Research · doi:10.1007/s11414-014-9449-0 · Cited by 19 · see citing works
The Impact of a Home Visiting Program on Children’s Utilization of Dental Services
Brickhouse, T. H., Haldiman, R. R., & Evani, B. (2013) · PEDIATRICS · doi:10.1542/peds.2013-1021n · Cited by 19 · see citing works
Martin, A. B., Hardin, J. W., Veschusio, C., & Kirby, H. (2012) · PubMed · Cited by 10 · see citing works
Preschool Education Studies: A Bibliography Organized by Research Strengths
Barnett, W. S. (2007) · Cited by 4 · see citing works
D’Agostino (1998) · doi:10.1002/(sici)1097-0258(19981015)17:19
Flagged first, then by citations
Research works printed but not linked: 1
- [11]Friedman JH Hastie T Tibshirani R Additive logistic regression: A statistical view of boosting Annals of Statistics 2000 28 337 374
Cited by
Sorted by citations
Impact of Early Head Start in North Carolina on dental care use among children younger than 3 years
Burgette, J. M., Preisser, J. S., Weinberger, M., King, R. S., Lee, J. Y., & Rozier, R. G. (2017) · American Journal of Public Health · doi:10.2105/ajph.2016.303621 · Cited by 15 · see citing works
Self-citationBurgette, J. M., Preisser, J. S., & Rozier, R. G. (2018) · The Journal of the American Dental Association · doi:10.1016/j.adaj.2018.07.019 · Cited by 9 · see citing works
Self-citation
- Authors
- Burgette, J. M., Preisser, J. S., & Rozier, R. G.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Burgette, Jacqueline M.
- Venue
- Journal of Public Health Dentistry
- Publisher
- Wiley
- Published
- 2015-06-18
- Volume
- 76
- Issue
- 1
- Pages
- 17-29
- ISSN
- 0022-4006 (ISSN-L)
- 1752-7325
- PMC
- PMC4684474
- OpenAlex
- W1501854446
- Cited by
- 25
- References
- 36
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 3 · 2018: 2 · 2017: 4 · 2016: 1
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Public Health Dentistry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Dental and Craniofacial Research (T32DE017245, R01DE018236)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- propensity score weighting, Early Head Start, generalized boosted models, logistic regression, covariate balance, confounding, effective sample size, early childhood intervention
- MeSH terms
- Algorithms, Female, Health Status Indicators, Humans, Infant, Interviews as Topic, Male, North Carolina, Logistic Models, Dental Care for Children, Propensity Score, Early Medical Intervention
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
