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Statistics in Medicine, 2009 The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates.
Peter C Austin
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SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING
Journal of Economic Surveys, 2008 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
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Statistics in Medicine, 2015 The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment ...
Peter C Austin, Elizabeth A Stuart
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Matching on the Estimated Propensity Score [PDF]
Econometrica, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alberto Abadie, Guido W. Imbens
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Applications of propensity score matching: a case series of articles published in [PDF]
Annals of Coloproctology, 2022 Propensity score matching (PSM) is an increasingly applied method of ensuring comparability between groups of interest. However, PSM is often applied unconditionally, without precise considerations.
Hwa Jung Kim
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JAMA, 2015 Two recent studies published in JAMA involved the analysis of observational data to estimate the effect of a treatment on patient outcomes. In the study by Roze et al,1 a large observational data set was analyzed to estimate the relationship between early echocardiography screening for patent ductus arteriosus and mortality among preterm infants.
Jason S, Haukoos, Roger J, Lewis
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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
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Demystifying propensity scores [PDF]
British Journal of Anaesthesia, 2014 Increasing availability of large clinical data sets is driving a proliferation of observational epidemiology studies in perioperative care. This wealth of data must be judged both on its inherent quality and the statistical techniques used to analyse the data set.
G N, Okoli, R D, Sanders, P, Myles
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Introduction to propensity scores [PDF]
Respirology, 2014 AbstractAlthough randomization provides a gold‐standard method of assessing causal relationships, it is not always possible to randomly allocate exposures. Where exposures are not randomized, estimating exposure effects is complicated by confounding. The traditional approach to dealing with confounding is to adjust for measured confounding variables ...
Elizabeth J, Williamson, Andrew, Forbes
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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
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