scholarly journals Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models

2021 ◽  
Author(s):  
Ellen C. Caniglia ◽  
Eleanor J. Murray ◽  
Miguel A. Hernán ◽  
Zach Shahn
Keyword(s):  
Resource Constraints ◽  
Treatment Strategies ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Dynamic Treatment ◽  
Optimal Dynamic

scholarly journals History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimens

2005 ◽  
Vol 1 (1) ◽  
Author(s):  
Mark J. van der Laan ◽  
Maya L Petersen ◽  
Marshall M Joffe
Keyword(s):  
Structural Models ◽  
Marginal Structural Models ◽  
Treatment Regimens ◽  
Dynamic Treatment ◽  
Optimal Dynamic

scholarly journals The causal effect of dynamic fertility treatment strategies on the probability of pregnancy: a novel application of marginal structural models (MSMs)

2019 ◽  
Vol 112 (3) ◽  
pp. e178
Author(s):  
Soudeh Ansari ◽  
Michael P. LaValley ◽  
Sara Lodi ◽  
Brooke Hayward ◽  
Gilbert L. Mottla ◽  
...  
Keyword(s):  
Causal Effect ◽  
Treatment Strategies ◽  
Structural Models ◽  
Fertility Treatment ◽  
Marginal Structural Models

scholarly journals Marginal Structural Models for Comparing Alternative Treatment Strategies in Ophthalmology using Observational Data

2013 ◽  
Vol 20 (4) ◽  
pp. 197-200 ◽  
Author(s):  
Marshall M. Joffe ◽  
Maxwell Pistilli ◽  
John H. Kempen
Keyword(s):  
Observational Data ◽  
Alternative Treatment ◽  
Treatment Strategies ◽  
Structural Models ◽  
Marginal Structural Models

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.

scholarly journals Influence Re-weighted G-Estimation

2016 ◽  
Vol 12 (1) ◽  
pp. 157-177
Author(s):  
Benjamin Rich ◽  
Erica E. M. Moodie ◽  
David A. Stephens
Keyword(s):  
Individualized Medicine ◽  
Treatment Strategies ◽  
Treatment Regimens ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
Optimal Dynamic ◽  
Data Adaptive ◽  
Doubly Robust ◽  
The Cost ◽  
The Impact

Abstract Individualized medicine is an area that is growing, both in clinical and statistical settings, where in the latter, personalized treatment strategies are often referred to as dynamic treatment regimens. Estimation of the optimal dynamic treatment regime has focused primarily on semi-parametric approaches, some of which are said to be doubly robust in that they give rise to consistent estimators provided at least one of two models is correctly specified. In particular, the locally efficient doubly robust g-estimation is robust to misspecification of the treatment-free outcome model so long as the propensity model is specified correctly, at the cost of an increase in variability. In this paper, we propose data-adaptive weighting schemes that serve to decrease the impact of influential points and thus stabilize the estimator. In doing so, we provide a doubly robust g-estimator that is also robust in the sense of Hampel (15).

Optimal Dynamic Treatment Strategies with Protection Against Missed Decision Points

2013 ◽  
Vol 6 (2) ◽  
pp. 261-289 ◽  
Author(s):  
Susanne Rosthøj ◽  
Robin Henderson ◽  
Jessica K. Barrett
Keyword(s):  
Treatment Strategies ◽  
Decision Points ◽  
Dynamic Treatment ◽  
Optimal Dynamic

scholarly journals Caesarean delivery and early childhood caries: Estimation with marginal structural models in Brazilian pre‐schoolers

2021 ◽  
Author(s):  
Lorena Lúcia Costa Ladeira ◽  
Sarah Pereira Martins ◽  
Cayara Mattos Costa ◽  
Elizabeth Lima Costa ◽  
Rubenice Amaral da Silva ◽  
...  
Keyword(s):  
Early Childhood ◽  
Caesarean Delivery ◽  
Early Childhood Caries ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Childhood Caries
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