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Propensity Score Analysis With Missing Data

Published on Jan 1, 2016in Psychological Methods
· DOI :10.1037/met0000076
Heining Cham15
Estimated H-index: 15
(Fordham University),
Stephen G. West68
Estimated H-index: 68
(ASU: Arizona State University)
Abstract
Propensity score analysis is a method that equates treatment and control groups on a comprehensive set of measured confounders in observational (nonrandomized) studies. A successful propensity score analysis reduces bias in the estimate of the average treatment effect in a nonrandomized study, making the estimate more comparable with that obtained from a randomized experiment. This article reviews and discusses an important practical issue in propensity analysis, in which the baseline covariates (potential confounders) and the outcome have missing values (incompletely observed). We review the statistical theory of propensity score analysis and estimation methods for propensity scores with incompletely observed covariates. Traditional logistic regression and modern machine learning methods (e.g., random forests, generalized boosted modeling) as estimation methods for incompletely observed covariates are reviewed. Balance diagnostics and equating methods for incompletely observed covariates are briefly described. Using an empirical example, the propensity score estimation methods for incompletely observed covariates are illustrated and compared. (PsycINFO Database Record(c) 2016 APA, all rights reserved). Language: en
  • References (73)
  • Citations (15)
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References73
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#2Jan N. Hughes (A&M: Texas A&M University)H-Index: 41
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#2Heining Cham (Fordham University)H-Index: 15
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A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates are measured, then methods that equate on propensity scores can achieve balance on a large set of covari...
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: Treatment noncompliance in randomized experiments threatens the validity of causal inference and the interpretability of treatment effects. This article provides a nontechnical review of 7 approaches: 3 traditional and 4 newer statistical analysis strategies. Traditional approaches include (a) intention-to-treat analysis (which estimates the effects of treatment assignment irrespective of treatment received), (b) as-treated analysis (which reassigns participants to groups reflecting the treatm...
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The Toolkit for Weighting and Analysis of Nonequivalent Groups, twang, contains a set of functions and procedures to support causal modeling of observational data through the estimation and evaluation of propensity scores and associated weights. This package was developed in 2004. After extensive use, it received a major update in 2012. This tutorial provides an introduction to twang and demonstrates its use through illustrative examples. The foundation to the methods supported by twang is the p...
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We address the problem of recoverability i.e. deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random. We employ a formal representation called 'Missingness Graphs' to explicitly portray the causal mechanisms responsible for missingness and to encode dependencies between these mechanisms and the variables being measured. Using this representation, we derive conditions that the graph should satisfy to ensure recoverability and devise algorit...
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