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Contrast Specific Propensity Scores
Shasha Han,Donald B. Rubin +1 more
TLDR
The use of contrast-specific propensity scores (CSPS) is proposed, which allows the creation of treatment groups of units that are balanced with respect to bifurcations of the specified contrasts and the multivariate space spanned by these bifURcations.Abstract:
Basic propensity score methodology is designed to balance multivariate pre-treatment covariates when comparing one active treatment with one control treatment. Practical settings often involve comparing more than two treatments, where more complicated contrasts than the basic treatment-control one,(1,-1), are relevant. Here, we propose the use of contrast-specific propensity scores (CSPS). CSPS allow the creation of treatment groups of units that are balanced with respect to bifurcations of the specified contrasts and the multivariate space spanned by them.read more
Citations
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Outcomes After Minimally-invasive Versus Open Pancreatoduodenectomy: A Pan-European Propensity Score Matched Study
Sjors Klompmaker,J. van Hilst,Ulrich F. Wellner,O.R.C. Busch,A. Coratti,M. D'Hondt,Safi Dokmak,Sebastiaan Festen,Mustafa Kerem,Igor Khatkov,D.J. Lips,Carlo Lombardo,M. Luyer,Alberto Manzoni,I.Q. Molenaar,Edoardo Rosso,O. Saint-Marc,Franky Vansteenkiste,Uwe A. Wittel,Bert A. Bonsing,B. Groot Koerkamp,M. Abu Hilal,David Fuks,Ignasi Poves,Tobias Keck,Ugo Boggi,Marc G. Besselink +26 more
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Automatic detection of influential actors in disinformation networks.
Steven T. Smith,Edward K. Kao,Erika Mackin,Danelle C. Shah,Olga Simek,Donald B. Rubin,Donald B. Rubin,Donald B. Rubin +7 more
TL;DR: A classifier that detects reported IO accounts with 96% precision, 79% recall, and 96% AUPRC is presented, demonstrated on real social media data collected for the 2017 French presidential election and known IO accounts disclosed by Twitter.
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Data-driven integrated care pathways: Standardization of delivering patient-centered care
Shasha Han,Libing Ma +1 more
TL;DR: This Viewpoint presents data-driven integrated care pathways as a potential solution to standardize patient-centered care delivery in China by highlighting five core aspects of the entire care journey for personalization by using real-time data and digital technology.
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City-Specific Effects of Lifting Mobility Restrictions — China, February–March 2020
TL;DR: In this paper , the effect of lifting mobility restrictions on COVID-19 death rate and incidence varied by city, with smaller increases or even reductions in cities with low community connectivity and small floating volume, and larger increases in cities having high community connectivity with large floating volume.
References
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Journal ArticleDOI
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
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Estimating causal effects of treatments in randomized and nonrandomized studies.
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
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An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
TL;DR: The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.
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Some practical guidance for the implementation of propensity score matching
Marco Caliendo,Sabine Kopeinig +1 more
TL;DR: Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects as discussed by the authors, but empirical examples can be found in very diverse fields of study, and each implementation step involves a lot of decisions and different approaches can be thought of.
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Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group
TL;DR: The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the variance of covariates in the two groups, and therefore reduce bias as mentioned in this paper.