Results 11 to 20 of about 3,722,033 (368)
medRxiv, 2021 Background: Thrombotic complications occur at high rates in hospitalized patients with COVID-19, yet the impact of intensive antithrombotic therapy on mortality is uncertain.
Matthew L. Meizlish +21 more
semanticscholar +2 more sources
Propensity score matching with R: conventional methods and new features
Annals of Translational Medicine, 2021 It is increasingly important to accurately and comprehensively estimate the effects of particular clinical treatments. Although randomization is the current gold standard, randomized controlled trials (RCTs) are often limited in practice due to ethical ...
Qin-yu Zhao +5 more
semanticscholar +1 more source
Agriculture & Food Security, 2021 Adoption of improved agricultural technologies remains to be a promising strategy to achieve food security and poverty reduction in many developing countries.
M. G. Wordofa +5 more
semanticscholar +1 more source
Propensity score methods for observational studies with clustered data: A review
Statistics in Medicine, 2022 Propensity score methods are a popular approach to mitigating confounding bias when estimating causal effects in observational studies. When study units are clustered (eg, patients nested within health systems), additional challenges arise such as ...
Ting-Hsuan Chang, E. Stuart
semanticscholar +1 more source
Applying Propensity Score Methods in Clinical Research in Neurology
Neurology, 2021 Propensity score–based analysis is increasingly being used in observational studies to estimate the effects of treatments, interventions, and exposures. We introduce the concept of the propensity score and how it can be used in observational research. We
P. Austin +3 more
semanticscholar +1 more source
A Comparison of Propensity Score and Linear Regression Analysis of Complex Survey Data
Journal of Data Science, 2021 We extend propensity score methodology to incorporate survey weights from complex survey data and compare the use of multiple linear regression and propensity score analysis to estimate treatment effects in observational data from a complex survey.
Elaine L. Zanutto
semanticscholar +1 more source
Propensity Scores: A Practical Introduction Using R
Journal of MultiDisciplinary Evaluation, 2015 Background: This paper provides an introduction to propensity scores for evaluation practitioners. Purpose: The purpose of this paper is to provide the reader with a conceptual and practical introduction to propensity scores, matching using propensity ...
Antonio Olmos, Priyalatha Govindasamy
doaj +1 more source
Multivariate Behavioral Research, 2011 The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular ...
P. Austin
semanticscholar +1 more source
An overview of propensity score matching methods for clustered data
Statistical Methods in Medical Research, 2022 Propensity score matching is commonly used in observational studies to control for confounding and estimate the causal effects of a treatment or exposure.
Benjamin Langworthy +2 more
semanticscholar +1 more source
Some Practical Guidance for the Implementation of Propensity Score Matching
Social Science Research Network, 2005 Propensity Score Matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study.
Marco Caliendo, Sabine Kopeinig
semanticscholar +1 more source