Results 271 to 280 of about 2,231,738 (318)
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Pharmaceutical statistics, 2023
Matching reduces confounding bias in comparing the outcomes of nonrandomized patient populations by removing systematic differences between them. Under very basic assumptions, propensity score (PS) matching can be shown to eliminate bias entirely in ...
S. Privitera +4 more
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Matching reduces confounding bias in comparing the outcomes of nonrandomized patient populations by removing systematic differences between them. Under very basic assumptions, propensity score (PS) matching can be shown to eliminate bias entirely in ...
S. Privitera +4 more
semanticscholar +1 more source
Covariate Balance for Observational Effectiveness Studies: A Comparison of Matching and Weighting
Journal of Research on Educational Effectiveness, 2022Propensity score matching and weighting methods are often used in observational effectiveness studies to reduce imbalance between treated and untreated groups on a set of potential confounders.
Joseph M. Kush +3 more
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Journal of Econometrics, 2021
Modern social and biomedical scientific publications require the reporting of covariate balance tables with not only covariate means by treatment group but also the associated $p$-values from significance tests of their differences. The practical need to
Anqi Zhao, Peng Ding
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Modern social and biomedical scientific publications require the reporting of covariate balance tables with not only covariate means by treatment group but also the associated $p$-values from significance tests of their differences. The practical need to
Anqi Zhao, Peng Ding
semanticscholar +1 more source
Covariates Distributions Balancing for Continuous Treatment
SSRN Electronic Journal, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qingshan Jiang, Li Xu, Can Huang
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Algorithms and Complexities of Matching Variants in Covariate Balancing
Operations Research, 2023In an observational study there are two disjointed groups of samples, one of treatment samples and the other of control samples. Each of the samples is characterized by several observed covariates. Covariate balancing problems arise when estimating causal effects using observational data.
Dorit S. Hochbaum, Asaf Levin, Xu Rao
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Balancing Unobserved Covariates With Covariate-Adaptive Randomized Experiments
Journal of the American Statistical Association, 2020Balancing important covariates is often critical in clinical trials and causal inference. Stratified permuted block (STR-PB) and covariate-adaptive randomization (CAR) procedures are widely used to...
Yang Liu, Feifang Hu
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Evaluation of subset matching methods and forms of covariate balance
Statistics in Medicine, 2016This paper conducts a Monte Carlo simulation study to evaluate the performance of multivariate matching methods that select a subset of treatment and control observations. The matching methods studied are the widely used nearest neighbor matching with propensity score calipers and the more recently proposed methods, optimal matching of an optimally ...
M. Resa, J. Zubizarreta
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How Well Can Fine Balance Work for Covariate Balancing
Biometrics, 2022Abstract Fine balance is a matching technique to improve covariate balance in observational studies. It constrains a match to have identical distributions for some covariates without restricting who is matched to whom. However, despite its wide application and excellent performance in practice, there is very little theory indicating when
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Balanced covariates with response adaptive randomization
Pharmaceutical Statistics, 2017AbstractResponse adaptive randomization (RAR) methods for clinical trials are susceptible to imbalance in the distribution of influential covariates across treatment arms. This can make the interpretation of trial results difficult, because observed differences between treatment groups may be a function of the covariates and not necessarily because of ...
Benjamin R. Saville, Scott M. Berry
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Statistical primer: propensity scores used as overlap weights provide exact covariate balance.
European Journal of Cardio-Thoracic SurgeryOverlap weighting (OW), using weights defined as the probability of receiving the opposite treatment, is a relatively new, alternative propensity score (PS)-based weighting technique used to adjust for confounding when estimating causal treatment effects.
Alexander Zajichek, G. Grunkemeier
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