The Propensity Score

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propensity score analysis

Methods for propensity score analysis (PSA) originated with Rosenbaum and Rubin (1983), as vehicles to sharpen and clarify treatment group comparisons in observational studies. Although highly recommended by many statisticians, and applied often in medical sciences, PSA has seen relatively few applications in the social and behavioral sciences. This paper aims to facilitate sound PSA applicatio...

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Covariate balancing propensity score

The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in the analysis of observational data. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers...

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Head to head comparison of the propensity score and the high-dimensional propensity score matching methods.

BACKGROUND Comparative performance of the traditional propensity score (PS) and high-dimensional propensity score (hdPS) methods in the adjustment for confounding by indication remains unclear. We aimed to identify which method provided the best adjustment for confounding by indication within the context of the risk of diabetes among patients exposed to moderate versus high potency statins. M...

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Overview of Propensity Score Methods

The propensity score methods are widely used to adjust confounding effects in observational studies when comparing treatment effects. The propensity score is defined as the probability of treatment assignment conditioning on some observed baseline characteristics and it provides a balanced score for the treatment conditions as conditioning on the propensity score, the treatment groups are compa...

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Propensity score based data analysis

For some time, propensity score (PS) based methods have been frequently applied in the analysis of observational and registry data. The PS is the conditional probability of a certain treatment given patient’s covariates. PS methods are used to eliminate imbalances in baseline covariate distributions between treatment groups and permit to estimate marginal effects. The package nonrandom is a too...

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ژورنال

عنوان ژورنال: JAMA

سال: 2015

ISSN: 0098-7484

DOI: 10.1001/jama.2015.13480