• Monte-Carlo Sensitivity Analys...

Treffer: Monte-Carlo Sensitivity Analysis for Controlled Direct Effects Using Marginal Structural Models in the Presence of Confounded Mediators

Title:
Monte-Carlo Sensitivity Analysis for Controlled Direct Effects Using Marginal Structural Models in the Presence of Confounded Mediators
Authors:
CHIBA, Yasutaka
Source:
Communications in statistics. Theory and methods. 41(10-12):1739-1749
Publisher Information:
Philadelphia, PA: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Généralités, General topics, Lois de probabilités, Distribution theory, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Algèbre linéaire numérique, Numerical linear algebra, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algèbre linéaire numérique, Numerical linear algebra, Algebra lineal numérica, Analyse numérique, Numerical analysis, Análisis numérico, Analyse sensibilité, Sensitivity analysis, Análisis sensibilidad, Distribution statistique, Statistical distribution, Distribución estadística, Efficacité traitement, Treatment efficiency, Eficacia tratamiento, Erreur systématique, Bias, Error sistemático, Estimation biaisée, Biased estimation, Estimación sesgada, Estimation statistique, Statistical estimation, Estimación estadística, Inversion matrice, Matrix inversion, Inversión matriz, Loi marginale, Marginal distribution, Ley marginal, Loi probabilité, Probability distribution, Ley probabilidad, Modèle structure, Structural model, Modelo estructura, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode directe, Direct method, Método directo, Méthode statistique, Statistical method, Método estadístico, Méthode stochastique, Stochastic method, Método estocástico, Plan randomisé, Randomized design, Plan aleatorizado, Probabilité, Probability, Probabilidad, Randomisation, Randomization, Aleatorización, Système linéaire, Linear system, Sistema lineal, Théorie approximation, Approximation theory, 62E17, 65C05, 65F05, Effet traitement, Treatment effect, 62P10, 92B15, Causal Inference, Inverse-probability-of-treatment-weighting, Potential outcome, Randomized trial
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Environmental Medicine and Behavioral Science, Kinki University School of Medicine, Osaka, Japan
ISSN:
0361-0926
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26036610
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.26036610
Database:
PASCAL Archive

Weitere Informationen

In randomized trials, investigators are frequently interested in estimating the direct effect of a treatment on an outcome that is not relayed by intermediate variables, in addition to the usual intention-to-treat (ITT) effect. Even if the ITT effect is not confounded due to randomization, the direct effect is not identified when unmeasured variables affect the intermediate and outcome variables. Although the unmeasured variables cannot be adjusted for in the models, it is still important to evaluate the potential bias of these variables quantitatively. This article proposes a sensitivity analysis method for controlled direct effects using a marginal structural model that is an extension of the sensitivity analysis method of unmeasured confounding introduced in the context of observational studies. The proposed method is illustrated using a randomized trial of depression.