Results 301 to 310 of about 206,310 (335)
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2011
Propensity-scores and propensity-score-matching can be used respectively for adjusting covariates in a multiple regression analysis and for stratification/matching of asymmetric observational clinical data, and have recently been emphasized by Dr. D’Agostino in an invited paper in Circulation as a promising additional tool for analyzing such data (D ...
Ton J. Cleophas, Aeilko H. Zwinderman
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Propensity-scores and propensity-score-matching can be used respectively for adjusting covariates in a multiple regression analysis and for stratification/matching of asymmetric observational clinical data, and have recently been emphasized by Dr. D’Agostino in an invited paper in Circulation as a promising additional tool for analyzing such data (D ...
Ton J. Cleophas, Aeilko H. Zwinderman
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Propensity score matching methodology
Video Journal of Biomedicine, 2022Learn more about propensity score matching and how it is used to evaluate data in observational studies that do not have an internal comparator in this animated video. To find out more about how the methodology was used to evaluate effectiveness data from a patient drug registry and an open-label study, please refer to the two research articles cited ...
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Nonparametric Bootstrap for Propensity Score Matching Estimators
SSRN Electronic Journal, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bodory, Hugo +3 more
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Biometrical journal. Biometrische Zeitschrift, 2019
Propensity score matching (PSM) and propensity score weighting (PSW) are popular tools to estimate causal effects in observational studies. We address two open issues: how to estimate propensity scores and assess covariate balance.
M. Cannas, B. Arpino
semanticscholar +1 more source
Propensity score matching (PSM) and propensity score weighting (PSW) are popular tools to estimate causal effects in observational studies. We address two open issues: how to estimate propensity scores and assess covariate balance.
M. Cannas, B. Arpino
semanticscholar +1 more source
Propensity Score Matching for Education Data: Worked Examples
Journal of Experimental Education, 2020Randomized controlled trials are not always feasible in educational research, so researchers must use alternative methods to study treatment effects.
Marvin G. Powell +2 more
semanticscholar +1 more source
Medical Care, 2003
Health services researchers are often interested in the effect of a treatment or a service in situations in which randomization is difficult or impossible. One useful alternative involves propensity score methods, a means for matching members of different groups based on a range of characteristics.
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Health services researchers are often interested in the effect of a treatment or a service in situations in which randomization is difficult or impossible. One useful alternative involves propensity score methods, a means for matching members of different groups based on a range of characteristics.
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Hepatology Research, 2019
The prognosis of hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) is still poor. We aimed to evaluate the impact of TACE combined with radiofrequency ablation (TACE+RFA) on the prognosis of HCC patients
S. Shimose +15 more
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The prognosis of hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) is still poor. We aimed to evaluate the impact of TACE combined with radiofrequency ablation (TACE+RFA) on the prognosis of HCC patients
S. Shimose +15 more
semanticscholar +1 more source
Propensity Score Matching with Time‐Dependent Covariates
Biometrics, 2005SummaryIn observational studies with a time‐dependent treatment and time‐dependent covariates, it is desirable to balance the distribution of the covariates at every time point. A time‐dependent propensity score based on the Cox proportional hazards model is proposed and used in risk set matching. Matching on this propensity score is shown to achieve a
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Propensity Scores and Propensity Score Matching for Assessing Multiple Confounders
2012In the Chap. 23 methods for assessing confounders were reviewed. Propensity score are ideal for assessing confounding, particularly, if multiple confounders are in a study. E.g., age and cardiovascular risk factors may not be similarly distributed in two treatment groups of a parallel-group study. Propensity score matching is used to make observational
Ton J. Cleophas, Aeilko H. Zwinderman
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