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2016•Экология человекаOpen access

PROPENSITY SCORE MATCHING AS A MODERN STATISTICAL METHOD FOR BIAS CONTROL IN OBSERVATIONAL STUDIES WITH CONTINUOUS OUTCOME VARIABLE

A M Grjibovski, С В Иванов, Maria A. Gorbatova, A. A. Dyussupov

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Abstract

The authors presents a propensity score matching (PSM) technique - an effective method to control the effect of confounding factors in observational studies. PSM has been shown to be as efficient as linear regression analysis, but can be performed using smaller samples. This article presents the basic principles of PSM and its practical application using STATA 13 software for the studies with continuous dependent variable.

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What this paper is about

The authors presents a propensity score matching (PSM) technique - an effective method to control the effect of confounding factors in observational studies. PSM has been shown to be as efficient as linear regression analysis, but can be performed using smaller samples. This article presents the basic principles of PSM and its practical application using STATA 13 software for the studies with continuous dependent variable.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The authors presents a propensity score matching (PSM) technique - an effective method to control the effect of confounding factors in observational studies. PSM has been shown to be as efficient as linear regression analysis, but can be performed using smaller samples. This article presents the basic principles of PSM and its practical application using STATA 13 software for the studies with continuous dependent variable.

Key concepts: Propensity score matching, Observational study, Confounding, Outcome (game theory), Matching (statistics), Statistics, Variable (mathematics), Control variable

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PROPENSITY SCORE MATCHING AS A MODERN STATISTICAL METHOD FOR BIAS CONTROL IN OBSERVATIONAL STUDIES WITH CONTINUOUS OUTCOME VARIABLE — Research Paper | ScholarLens