Nonparametric likelihood and doubly robust estimating equations for marginal and nested structural models

2010 ◽  
Vol 38 (4) ◽  
pp. 609-632 ◽  
Author(s):  
Zhiqiang Tan
Keyword(s):  
Structural Models ◽  
Estimating Equations ◽  
Nonparametric Likelihood ◽  
Doubly Robust

scholarly journals Doubly Robust and Efficient Estimation of Marginal Structural Models for the Hazard Function

2016 ◽  
Vol 12 (1) ◽  
pp. 233-252 ◽  
Author(s):  
Wenjing Zheng ◽  
Maya Petersen ◽  
Mark J. van der Laan
Keyword(s):  
Maximum Likelihood ◽  
Hazard Function ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Targeted Maximum Likelihood ◽  
Dynamic Treatment ◽  
Doubly Robust

Abstract In social and health sciences, many research questions involve understanding the causal effect of a longitudinal treatment on mortality (or time-to-event outcomes in general). Often, treatment status may change in response to past covariates that are risk factors for mortality, and in turn, treatment status may also affect such subsequent covariates. In these situations, Marginal Structural Models (MSMs), introduced by Robins (1997. Marginal structural models Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1–10), are well-established and widely used tools to account for time-varying confounding. In particular, a MSM can be used to specify the intervention-specific counterfactual hazard function, i. e. the hazard for the outcome of a subject in an ideal experiment where he/she was assigned to follow a given intervention on their treatment variables. The parameters of this hazard MSM are traditionally estimated using the Inverse Probability Weighted estimation Robins (1999. Marginal structural models versus structural nested models as tools for causal inference. In: Statistical models in epidemiology: the environment and clinical trials. Springer-Verlag, 1999:95–134), Robins et al. (2000), (IPTW, van der Laan and Petersen (2007. Causal effect models for realistic individualized treatment and intention to treat rules. Int J Biostat 2007;3:Article 3), Robins et al. (2008. Estimaton and extrapolation of optimal treatment and testing strategies. Statistics in Medicine 2008;27(23):4678–721)). This estimator is easy to implement and admits Wald-type confidence intervals. However, its consistency hinges on the correct specification of the treatment allocation probabilities, and the estimates are generally sensitive to large treatment weights (especially in the presence of strong confounding), which are difficult to stabilize for dynamic treatment regimes. In this paper, we present a pooled targeted maximum likelihood estimator (TMLE, van der Laan and Rubin (2006. Targeted maximum likelihood learning. The International Journal of Biostatistics 2006;2:1–40)) for MSM for the hazard function under longitudinal dynamic treatment regimes. The proposed estimator is semiparametric efficient and doubly robust, offering bias reduction over the incumbent IPTW estimator when treatment probabilities may be misspecified. Moreover, the substitution principle rooted in the TMLE potentially mitigates the sensitivity to large treatment weights in IPTW. We compare the performance of the proposed estimator with the IPTW and a on-targeted substitution estimator in a simulation study.

Doubly Robust and Multiple-Imputation-Based Generalized Estimating Equations

2011 ◽  
Vol 21 (2) ◽  
pp. 202-225 ◽  
Author(s):  
Teshome Birhanu ◽  
Geert Molenberghs ◽  
Cristina Sotto ◽  
Michael G. Kenward
Keyword(s):  
Multiple Imputation ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Doubly Robust ◽  
Generalized Estimating

Validation of the Short Dark Triad in a German Sample

2020 ◽  
pp. 1-12
Author(s):  
Caroline Wehner ◽  
Ulrike Maaß ◽  
Marius Leckelt ◽  
Mitja D. Back ◽  
Matthias Ziegler
Keyword(s):  
Sensation Seeking ◽  
Factor Model ◽  
Structural Models ◽  
Self Esteem ◽  
Dark Triad ◽  
Fit Indices ◽  
Nomological Network ◽  
Competitive Test ◽  
Three Factor ◽  
Behavioral Criteria

Abstract. The structure, correlates, and assessment of the Dark Triad are widely discussed in several fields of psychology. Based on the German version of the Short Dark Triad (SDT), we add to this by (a) providing a competitive test of existing structural models, (b) testing the nomological network, and (c) proposing an ultrashort 9-item version of the SDT (uSDT). A sample of N = 969 participants provided data on the SDT and a range of further measures. Our competitive test of five structural models revealed that fit indices and nomological network assumptions were best met in a three-factor model, with separate factors for psychopathy, Machiavellianism, and narcissism. The results provided an extensive overview of the raw, unique, and shared associations of Dark Triad dimensions with narcissism facets, sadism, impulsivity, self-esteem, sensation seeking, the Big Five, maladaptive personality traits, sociosexual orientation, and behavioral criteria. Finally, the uSDT exhibited satisfactory psychometric properties. The highest overlap in expected relations between SDT and uSDT, and convergent and discriminant measures was also found for the three-factor model. Our study underlines the utility of a three-factor model of the Dark Triad, extends findings on its nomological network, and provides an ultrashort instrument.

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