A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study

2007 ◽  
Vol 26 (4) ◽  
pp. 734-753 ◽  
Author(s):  
Peter C. Austin ◽  
Paul Grootendorst ◽  
Geoffrey M. Anderson
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Monte Carlo Study

Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study

2007 ◽  
Vol 26 (4) ◽  
pp. 754-768 ◽  
Author(s):  
Peter C. Austin ◽  
Paul Grootendorst ◽  
Sharon-Lise T. Normand ◽  
Geoffrey M. Anderson
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Treatment Effect ◽  
Monte Carlo Study ◽  
Biased Estimation

Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study

2012 ◽  
Vol 11 (3) ◽  
pp. 222-229 ◽  
Author(s):  
Etienne Gayat ◽  
Matthieu Resche-Rigon ◽  
Jean-Yves Mary ◽  
Raphaël Porcher
Keyword(s):  
Monte Carlo ◽  
Data Analysis ◽  
Propensity Score ◽  
Survival Data ◽  
Proportional Hazards ◽  
Monte Carlo Study ◽  
Proportional Hazards Models

scholarly journals Optimal Caliper Width for Propensity Score Matching of Three Treatment Groups: A Monte Carlo Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e81045 ◽  
Author(s):  
Yongji Wang ◽  
Hongwei Cai ◽  
Chanjuan Li ◽  
Zhiwei Jiang ◽  
Ling Wang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Monte Carlo Study ◽  
Treatment Groups

scholarly journals How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study

2015 ◽  
Vol 50 (5) ◽  
pp. 520-532 ◽  
Author(s):  
Patricia Rodríguez De Gil ◽  
Aarti P. Bellara ◽  
Rheta E. Lanehart ◽  
Reginald S. Lee ◽  
Eun Sook Kim ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Propensity Score ◽  
Monte Carlo Study ◽  
Propensity Score Methods

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

Detecting Between-Groups Heteroscedasticity in MMR: A Monte Carlo Study

2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study

Monte Carlo study of 50 nm-long single and dual-gate MODFETs: suppression of short-channel effects

1993 ◽  
Vol 3 (9) ◽  
pp. 1719-1728
Author(s):  
P. Dollfus ◽  
P. Hesto ◽  
S. Galdin ◽  
C. Brisset
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Short Channel Effects ◽  
Short Channel ◽  
Channel Effects ◽  
Dual Gate
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