MD2 COMPARISON OF COVARIATE BALANCE AMONG PROPENSITY SCORE MATCHING VERSUS PROPENSITY SCORE WEIGHTING AND STRATIFICATION IN OBSERVATIONAL MEDICAL DEVICE RESEARCH

Resumo Este artigo analisa as diferenças de salário e alocação de tempo entre casados e solteiros no Brasil, com o objetivo de entender como o casamento está associado à distribuição de salários, jornada e tarefa doméstica dos homens e mulheres. Utilizamos dados da Pesquisa Nacional por Amostra de Domicílios (PNAD) para o ano de 2015 e aplicamos três diferentes métodos de estimação para amostras complexas: mínimos quadrados ordinários, Propensity Score Weighting e Propensity Score Matching. Os resultados indicam que homens casados apresentam salário e jornada de trabalho consistentemente superiores aos homens solteiros, enquanto mulheres casadas aumentam sua probabilidade de exercer tarefa doméstica, bem como a jornada de trabalho doméstico. Também encontramos evidências de que a alocação de tempo dos cônjuges em relação ao mercado de trabalho é afetada pela vantagem comparativa do casal.

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Propensity score matching in otolaryngologic literature: A systematic review and critical appraisal

PLoS ONE ◽

10.1371/journal.pone.0244423 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0244423

Author(s):

Aman Prasad ◽

Max Shin ◽

Ryan M. Carey ◽

Kevin Chorath ◽

Harman Parhar ◽

...

Keyword(s):

Systematic Review ◽

Propensity Score ◽

Propensity Score Matching ◽

Observational Studies ◽

Critical Appraisal ◽

Propensity Scores ◽

Final Analysis ◽

Publication History ◽

Covariate Balance

Background Propensity score techniques can reduce confounding and bias in observational studies. Such analyses are able to measure and balance pre-determined covariates between treated and untreated groups, leading to results that can approximate those generated by randomized prospective studies when such trials are not feasible. The most commonly used propensity score -based analytic technique is propensity score matching (PSM). Although PSM popularity has continued to increase in medical literature, improper methodology or methodological reporting may lead to biased interpretation of treatment effects or limited scientific reproducibility and generalizability. In this study, we aim to characterize and assess the quality of PSM methodology reporting in high-impact otolaryngologic literature. Methods PubMed and Embase based systematic review of the top 20 journals in otolaryngology, as measured by impact factor from the Journal Citations Reports from 2012 to 2018, for articles using PSM analysis throughout their publication history. Eligible articles were reviewed and assessed for quality and reporting of PSM methodology. Results Our search yielded 101 studies, of which 92 were eligible for final analysis and review. The proportion of studies utilizing PSM increased significantly over time (p < 0.001). Nearly all studies (96.7%, n = 89) specified the covariates used to calculate propensity scores. Covariate balance was illustrated in 67.4% (n = 62) of studies, most frequently through p-values. A minority (17.4%, n = 16) of studies were found to be fully reproducible according to previously established criteria. Conclusions While PSM analysis is becoming increasingly prevalent in otolaryngologic literature, the quality of PSM methodology reporting can be improved. We provide potential recommendations for authors regarding optimal reporting for analyses using PSM.

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A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting

Biometrical Journal ◽

10.1002/bimj.201800132 ◽

2019 ◽

Author(s):

Massimo Cannas ◽

Bruno Arpino

Keyword(s):

Machine Learning ◽

Propensity Score ◽

Propensity Score Matching ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Covariate Balance

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The Balance Super Learner: A robust adaptation of the Super Learner to improve estimation of the average treatment effect in the treated based on propensity score matching

Statistical Methods in Medical Research ◽

10.1177/0962280216682055 ◽

2016 ◽

Vol 27 (8) ◽

pp. 2504-2518 ◽

Cited By ~ 7

Author(s):

Romain Pirracchio ◽

Marco Carone

Keyword(s):

Propensity Score ◽

Propensity Score Matching ◽

Simulation Study ◽

Model Misspecification ◽

Average Treatment Effect ◽

Machine Learning Algorithms ◽

Propensity Score Model ◽

Covariate Balance ◽

Ensemble Machine Learning ◽

Super Learner

Consistency of the propensity score estimators rely on correct specification of the propensity score model. The propensity score is frequently estimated using a main effect logistic regression. It has recently been shown that the use of ensemble machine learning algorithms, such as the Super Learner, could improve covariate balance and reduce bias in a meaningful manner in the case of serious model misspecification for treatment assignment. However, the loss functions normally used by the Super Learner may not be appropriate for propensity score estimation since the goal in this problem is not to optimize propensity score prediction but rather to achieve the best possible balance in the covariate distribution between treatment groups. In a simulation study, we evaluated the benefit of a modification of the Super Learner by propensity score estimation geared toward achieving covariate balance between the treated and untreated after matching on the propensity score. Our simulation study included six different scenarios characterized by various degrees of deviation from the usual main term logistic model for the true propensity score and outcome as well as the presence (or not) of instrumental variables. Our results suggest that the use of this adapted Super Learner to estimate the propensity score can further improve the robustness of propensity score matching estimators.

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Evaluating the Utility of Coarsened Exact Matching for Pharmacoepidemiology Using Real and Simulated Claims Data

American Journal of Epidemiology ◽

10.1093/aje/kwz268 ◽

2019 ◽

Vol 189 (6) ◽

pp. 613-622 ◽

Cited By ~ 1

Author(s):

John E Ripollone ◽

Krista F Huybrechts ◽

Kenneth J Rothman ◽

Ryan E Ferguson ◽

Jessica M Franklin

Keyword(s):

Propensity Score ◽

Propensity Score Matching ◽

Mahalanobis Distance ◽

Claims Data ◽

The Elderly ◽

Effect Estimate ◽

Exact Matching ◽

Coarsened Exact Matching ◽

Covariate Balance ◽

Insurance Claims Data

Abstract Coarsened exact matching (CEM) is a matching method proposed as an alternative to other techniques commonly used to control confounding. We compared CEM with 3 techniques that have been used in pharmacoepidemiology: propensity score matching, Mahalanobis distance matching, and fine stratification by propensity score (FS). We evaluated confounding control and effect-estimate precision using insurance claims data from the Pharmaceutical Assistance Contract for the Elderly (1999–2002) and Medicaid Analytic eXtract (2000–2007) databases (United States) and from simulated claims-based cohorts. CEM generally achieved the best covariate balance. However, it often led to high bias and low precision of the risk ratio due to extreme losses in study size and numbers of outcomes (i.e., sparse data bias)—especially with larger covariate sets. FS usually was optimal with respect to bias and precision and always created good covariate balance. Propensity score matching usually performed almost as well as FS, especially with higher index exposure prevalence. The performance of Mahalanobis distance matching was relatively poor. These findings suggest that CEM, although it achieves good covariate balance, might not be optimal for large claims-database studies with rich covariate information; it might be ideal if only a few (<10) strong confounders must be controlled.

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Besondere Förderung von Kern- kompetenzen an Spezialgymnasien?

Zeitschrift für Pädagogische Psychologie ◽

10.1024/1010-0652/a000001 ◽

2010 ◽

Vol 24 (1) ◽

pp. 5-22 ◽

Cited By ~ 7

Author(s):

Jürgen Baumert ◽

Michael Becker ◽

Marko Neumann ◽

Roumiana Nikolova

Keyword(s):

Propensity Score ◽

Propensity Score Matching

Der vorliegende Beitrag geht der Frage nach, ob Schülerinnen und Schüler, die nach der vierten Klasse in Berlin in ein grundständiges Gymnasium wechseln, in Abhängigkeit vom Profil des besuchten Gymnasiums im Vergleich zu Grundschülern mit vergleichbaren Lernvoraussetzungen unterschiedliche Lernzuwächse im Leseverständnis, in Mathematik und Englisch erreichen. Auf der Datengrundlage der ELEMENT-Studie wurde die Leistungsentwicklung von Schülerinnen und Schülern an grundständigen Gymnasien (N = 1758) und Grundschulen (N = 3169) während der 5. und 6. Jahrgangsstufe mithilfe von Propensity Score Matching-Analysen (PSM) modelliert. Nach Kontrolle von leistungsrelevanten Unterschieden zwischen den Schülergruppen am Ende der 4. Jahrgangsstufe zeigten sich für das Leseverständnis am Ende der 6. Klasse keine statistisch signifikanten Unterschiede. Für die Mathematikleistung ließen sich Unterschiede lediglich zugunsten eines grundständigen Gymnasiums, das zum Untersuchungszeitpunkt noch kein spezifisches Profil entwickelt hatte, nachweisen. In der Domäne Englisch, in der die curricularen Unterschiede zwischen den Schulzweigen stärker akzentuiert sind, wurden positive Ergebnisse im Vergleich zu den Grundschulen für die so genannten Schnellläuferzüge, die englisch-bilingualen Klassen und das grundständige Gymnasium ohne spezifisches Profil ermittelt. Die Lernstände am Ende der 6. Klasse in den altsprachlichen Gymnasien fielen dagegen im Vergleich zu den Grundschulen geringer aus. Die Befunde widersprechen der Annahme, dass mit dem frühen Übergang auf ein grundständiges Gymnasium automatisch eine besondere Förderung der Lesefähigkeit und des mathematischen Verständnisses besonders leistungsfähiger Schülerinnen und Schüler erreicht werde. Die Ergebnisse zu den Englischleistungen weisen hingegen darauf hin, dass Unterschiede in der Leistungsentwicklung auftreten können, sofern die Aufteilung auf Schulen mit unterschiedlichen Bildungsprogrammen mit curricularen Unterschieden im Unterricht einhergeht. Methodische und inhaltliche Implikationen der Befunde und Grenzen ihrer Generalisierbarkeit werden diskutiert.

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Validating Four Standard Scales in Spiritually Practicing and Nonpracticing Samples Using Propensity Score Matching

European Journal of Psychological Assessment ◽

10.1027/1015-5759.24.3.165 ◽

2008 ◽

Vol 24 (3) ◽

pp. 165-173 ◽

Cited By ~ 8

Author(s):

Niko Kohls ◽

Harald Walach

Keyword(s):

Propensity Score ◽

Propensity Score Matching ◽

Repeated Measures ◽

Propensity Scores ◽

Total Sample ◽

Experimental Manipulation ◽

Brief Symptom Inventory ◽

Time Interaction ◽

Social Support Scale ◽

Lower Test

Validation studies of standard scales in the particular sample that one is studying are essential for accurate conclusions. We investigated the differences in answering patterns of the Brief-Symptom-Inventory (BSI), Transpersonal Trust Scale (TPV), Sense of Coherence Questionnaire (SOC), and a Social Support Scale (F-SoZu) for a matched sample of spiritually practicing (SP) and nonpracticing (NSP) individuals at two measurement points (t1, t2). Applying a sample matching procedure based on propensity scores, we selected two sociodemographically balanced subsamples of N = 120 out of a total sample of N = 431. Employing repeated measures ANOVAs, we found an intersample difference in means only for TPV and an intrasample difference for F-SoZu. Additionally, a group × time interaction effect was found for TPV. While Cronbach’s α was acceptable and comparable for both samples, a significantly lower test-rest-reliability for the BSI was found in the SP sample (rSP = .62; rNSP = .78). Thus, when researching the effects of spiritual practice, one should not only look at differences in means but also consider time stability. We recommend propensity score matching as an alternative for randomization in variables that defy experimental manipulation such as spirituality.

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