Results 21 to 30 of about 129,849 (273)
Variance reduction in randomised trials by inverse probability weighting using the propensity score. [PDF]
, 2013 In individually randomised controlled trials, adjustment for baseline characteristics is often undertaken to increase precision of the treatment effect estimate. This is usually performed using covariate adjustment in outcome regression models.
Forbes, Andrew +2 more
core +1 more source
European Journal of Radiology Open, 2018 Purpose: To assess the filter tilting and outcomes of the Celect and Denali inferior vena cava (IVC) filters by using a propensity score-matching analysis.
Jae Heung Bae, Sang Yub Lee
doaj +1 more source
A Bayesian view of doubly robust causal inference [PDF]
, 2016 In causal inference confounding may be controlled either through regression adjustment in an outcome model, or through propensity score adjustment or inverse probability of treatment weighting, or both. The latter approaches, which are based on modelling
Belzile, Léo R. +2 more
core +2 more sources
Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles
Mathematics, 2022 Many surveys are performed using non-probability methods such as web surveys, social networks surveys, or opt-in panels. The estimates made from these data sources are usually biased and must be adjusted to make them representative of the target ...
María del Mar Rueda +2 more
doaj +1 more source
MethodsX, 2020 Users of newly marketed drugs often differ from the patients included in randomized clinical trials, and from patients prescribed similar drugs. Cohorts of such users may be compared using propensity score adjustment, or similar user cohorts may be built
Patrick Blin +5 more
doaj +1 more source
F1000Research, 2020 Background: Interest exists in whether youth e-cigarette use (“vaping”) increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we previously reported adjustment for vaping propensity using Wave 1 variables explained ...
Peter N Lee, John S Fry
doaj +1 more source
Propensity scores with misclassified treatment assignment: a likelihood-based adjustment [PDF]
Biostatistics, 2017 Propensity score methods are widely used in comparative effectiveness research using claims data. In this context, the inaccuracy of procedural or billing codes in claims data frequently misclassifies patients into treatment groups, that is, the treatment assignment ($T$) is often measured with error. In the context of a validation data where treatment
Danielle, Braun +5 more
openaire +2 more sources
Cutting Feedback in Bayesian Regression Adjustment for the Propensity Score [PDF]
The International Journal of Biostatistics, 2010 McCandless, Gustafson and Austin (2009) describe a Bayesian approach to regression adjustment for the propensity score to reduce confounding. A unique property of the method is that the treatment and outcome models are combined via Bayes theorem. However, this estimation procedure can be problematic if the outcome model is misspecified.
McCandless, Lawrence C +3 more
openaire +2 more sources
High-dimensional propensity score adjustment in HIV research using linked administrative health data
International Journal of Population Data Science, 2019 Background Despite not being collected for research purposes, linked administrative health data are increasingly being used to conduct observational epidemiologic analyses.
Taylor McLinden +2 more
doaj +1 more source
Propensity score adjustment with several follow-ups
Biometrika, 2014 Summary: Propensity score weighting adjustment is commonly used to handle unit nonresponse. When the response mechanism is nonignorable in the sense that the response probability depends directly on the study variable, a follow-up sample is commonly used to obtain an unbiased estimator using the framework of two-phase sampling, where the follow-up ...
Kim, Jae Kwang, Im, Jongho
openaire +4 more sources