Marginal structural models are a class of statistical models used for causal inference in epidemiology. Such models handle the issue of timeâ€¦Â (More)

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Review

2016

Review

2016

Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventions administered over time. Inâ€¦Â (More)

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2014

2014

Saarela et al. are concerned with integrating propensity scores into a Bayesian framework. Some of us have previously writtenâ€¦Â (More)

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2013

2013

Marginal structural models (MSMs) are becoming increasingly popular as a tool to make causal inference from longitudinal dataâ€¦Â (More)

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2010

2010

One of the main objectives in clinical epidemiology is to detect a relation between treatment and outcome. We address data whereâ€¦Â (More)

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2010

2010

- Thaddeus J. Haight, Yue Wang, Mark J. van der Laan, Ira B. Tager
- Computational Statistics & Data Analysis
- 2010

The cross-validation deletion-substitution-addition (cvDSA) algorithm is based on data-adaptive estimation methodology to selectâ€¦Â (More)

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2010

2010

Sufficient cause interactions concern cases in which there is a particular causal mechanism for some outcome that requires theâ€¦Â (More)

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2009

2009

- Kjetil RÃ¸ysland
- 2009

Marginal structural models were introduced in order to provide estimates of causal effects from interventions based onâ€¦Â (More)

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2008

2008

The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selectionâ€¦Â (More)

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2005

2005

- Zoe Fewell, Miguel A. HernÃ¡n, +6 authors J. A. C. Sterne
- 2005

Longitudinal studies in which exposures, confounders, and outcomes are measured repeatedly over time have the potential to allowâ€¦Â (More)

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Highly Cited

1999

Highly Cited

1999

- James M. Robins
- 1999

Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMsâ€¦Â (More)

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