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	Provedor de dados AgEcon (2) Autor Choi, Hyon (1) Dunn, Graham (1) Emsley, Richard (1) Fewell, Zoe (1) Hernan, Miguel A. (1) Lunt, Mark (1) Pickles, Andrew (1) Sterne, Jonathan A.C. (1) Tilling, Kate (1) Wolfe, Frederick (1) Mais... Palavra-chave Causal models (2) Confounding (2) Research Methods/ Statistical Methods (2) Double-robust estimators (1) Dr (1) Inverse probability of treatment weights (1) Logistic regression (1) Marginal structural models (1) Propensity score (1) Survival analysis (1) Weighted regression (1) Mais... Tipo do documento Article (1) Journal Article (1) Ano 2008 (1) 2004 (1) País United States (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Implementing double-robust estimators of causal effects AgEcon Emsley, Richard; Lunt, Mark; Pickles, Andrew; Dunn, Graham. This article describes the implementation of a double-robust estimator for pretest–posttest studies (Lunceford and Davidian, 2004, Statistics in Medicine 23: 2937–2960) and presents a new Stata command (dr) that carries out the procedure. A double-robust estimator gives the analyst two opportunities for obtaining unbiased inference when adjusting for selection effects such as confounding by allowing for different forms of model misspecification; a double-robust estimator also can offer increased efficiency when all the models are correctly specified. We demonstrate the results with a Monte Carlo simulation study, and we show how to implement the double-robust estimator on a single simulated dataset, both manually and by using the dr command. Tipo: Article Palavras-chave: Dr; Double-robust estimators; Causal models; Confounding; Inverse probability of treatment weights; Propensity score; Research Methods/ Statistical Methods. Ano: 2008 URL: http://purl.umn.edu/122597 Controlling for time-dependent confounding using marginal structural models AgEcon Fewell, Zoe; Hernan, Miguel A.; Wolfe, Frederick; Tilling, Kate; Choi, Hyon; Sterne, Jonathan A.C.. Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time have the potential to allow causal inferences about the effects of exposure on outcome. There is particular interest in estimating the causal effects of medical treatments (or other interventions) in circumstances in which a randomized controlled trial is difficult or impossible. However, standard methods for estimating exposure effects in longitudinal studies are biased in the presence of time-dependent confounders affected by prior treatment. This article describes the use of marginal structural models (described by Robins, Hernán, and Brumback [2000]) to estimate exposure or treatment effects in the presence of time-dependent confounders affected by... Tipo: Journal Article Palavras-chave: Marginal structural models; Causal models; Weighted regression; Survival analysis; Logistic regression; Confounding; Research Methods/ Statistical Methods. Ano: 2004 URL: http://purl.umn.edu/116267 Registros recuperados: 2 Primeira ... 1 ... Última

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