Marginal structural models for estimating principal stratum direct effects under the monotonicity assumption

2011 ◽  
Vol 53 (6) ◽  
pp. 1025-1034 ◽  
Author(s):  
Yasutaka Chiba
Keyword(s):  
Structural Models ◽  
Marginal Structural Models ◽  
Direct Effects ◽  
Monotonicity Assumption

scholarly journals Estimating controlled direct effects through marginal structural models

2020 ◽  
Vol 8 (3) ◽  
pp. 391-408
Author(s):  
Michelle Torres
Keyword(s):  
Regression Models ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Time Varying ◽  
Direct Effects ◽  
Treatment Control ◽  
Regression Methods ◽  
Confounding Bias ◽  
Unbiased Estimates ◽  
Future Outcomes

AbstractWhen working with panel data, many researchers wish to estimate the direct effects of time-varying factors on future outcomes. However, when a baseline treatment affects both the confounders of further stages of the treatment and the outcome, the estimation of controlled direct effects (CDEs) using traditional regression methods faces a bias trade-off between confounding bias and post-treatment control. Drawing on research from the field of epidemiology, in this article I present a marginal structural modeling (MSM) approach that allows scholars to generate unbiased estimates of CDEs. Further, I detail the characteristics and implementation of MSMs, compare the performance of this approach under different conditions, and discuss and assess practical challenges when conducting them. After presenting the method, I apply MSMs to estimate the effect of wealth in childhood on political participation, highlighting the improvement in terms of bias relative to traditional regression models. The analysis shows that MSMs improve our understanding of causal mechanisms especially when dealing with multi-categorical time-varying treatments and non-continuous outcomes.

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

Rejoinder “On Bayesian estimation of marginal structural models”

Biometrics ◽  
2015 ◽  
Vol 71 (2) ◽  
pp. 299-301 ◽  
Author(s):  
Olli Saarela ◽  
David A. Stephens ◽  
Erica E. M. Moodie ◽  
Marina B. Klein
Keyword(s):  
Bayesian Estimation ◽  
Structural Models ◽  
Marginal Structural Models

scholarly journals Causal inference in continuous time: an example on prostate cancer therapy

Biostatistics ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 172-185 ◽  
Author(s):  
Pål Christie Ryalen ◽  
Mats Julius Stensrud ◽  
Sophie Fosså ◽  
Kjetil Røysland
Keyword(s):  
Prostate Cancer ◽  
Continuous Time ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Cancer Treatments ◽  
Discrete Parameter ◽  
Inverse Probability Weights ◽  
Using Data ◽  
Prostate Cancer Treatments

Abstract In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time marginal structural models. Bernoulli 17, 895–915) developed the continuous-time MSMs, along with continuous-time weights. The continuous-time weights are conceptually similar to the inverse probability weights that are used in discrete time MSMs. Here, we demonstrate that continuous-time MSMs may be used in practice. First, we briefly describe the causal model assumptions using counting process notation, and we suggest how causal effect estimates can be derived by calculating continuous-time weights. Then, we describe how additive hazard models can be used to find such effect estimates. Finally, we apply this strategy to compare medium to long-term differences between the two prostate cancer treatments radical prostatectomy and radiation therapy, using data from the Norwegian Cancer Registry. In contrast to the results of a naive analysis, we find that the marginal cumulative incidence of treatment failure is similar between the strategies, accounting for the competing risk of other death.

Marginal Structural Models and Causal Inference in Epidemiology

Epidemiology ◽  
2000 ◽  
Vol 11 (5) ◽  
pp. 550-560 ◽  
Author(s):  
James M. Robins ◽  
Miguel Ángel Hernán ◽  
Babette Brumback
Keyword(s):  
Causal Inference ◽  
Structural Models ◽  
Marginal Structural Models
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