Estimation of Direct Effects for Survival Data by using the Aalen Additive Hazards Model

Martinussen, Torben; Vansteelandt, Stijn; Gerster, Mette; Hjelmborg, Jacob

doi:10.1111/j.1467-9868.2011.00782.x

Cited by 44 publications

(55 citation statements)

References 36 publications

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“…Numerous other methods have been developed (1,2,9,13,24,26,35,58), which we could not address in this article. Some of these are discussed in the book length treatment of mediation (47).…”

Section: Resultsmentioning

confidence: 99%

Mediation Analysis: A Practitioner's Guide

VanderWeele

2016

Annu. Rev. Public Health

1,294

961

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This article provides an overview of recent developments in mediation analysis, that is, analyses used to assess the relative magnitude of different pathways and mechanisms by which an exposure may affect an outcome. Traditional approaches to mediation in the biomedical and social sciences are described. Attention is given to the confounding assumptions required for a causal interpretation of direct and indirect effect estimates. Methods from the causal inference literature to conduct mediation in the presence of exposuremediator interactions, binary outcomes, binary mediators, and case-control study designs are presented. Sensitivity analysis techniques for unmeasured confounding and measurement error are introduced. Discussion is given to extensions to time-to-event outcomes and multiple mediators. Further flexible modeling strategies arising from the precise counterfactual definitions of direct and indirect effects are also described. The focus throughout is on methodology that is easily implementable in practice across a broad range of potential applications.

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“…Numerous other methods have been developed (1,2,9,13,24,26,35,58), which we could not address in this article. Some of these are discussed in the book length treatment of mediation (47).…”

Section: Resultsmentioning

confidence: 99%

Mediation Analysis: A Practitioner's Guide

VanderWeele

2016

Annu. Rev. Public Health

1,294

961

View full text Add to dashboard Cite

show abstract

“…Still, there is some evidence that HAART also has beneficial effects that are not mediated through virus suppression, i.e. direct effects in our context (Lu and Andrieu, 2000; Monini et al. , 2003; Bernstein and Dennis, 2008).…”

Section: Direct and Indirect Effects–mediationmentioning

confidence: 88%

Causality, Mediation and Time: A Dynamic Viewpoint

Aalen

Røysland

Gran

et al. 2012

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must operate in time and we show how this corresponds to a mechanistic, or system, understanding of causality. The established counterfactual definitions of direct and indirect effects depend on an ability to manipulate the mediator which may not hold in practice, and we argue that a mechanistic view may be better. Graphical representations based on local independence graphs and dynamic path analysis are used to facilitate communication as well as providing an overview of the dynamic relations ‘at a glance’. The relationship between causality as understood in a mechanistic and in an interventionist sense is discussed. An example using data from the Swiss HIV Cohort Study is presented.

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“…If we substitute the counterfactual survival indicators S k , g =( R̄ m , 0 ) = 1 − R k , g =( R̄ m , 0 ) and S k , g =( R̄ m −1 , 0 ) = 1 − R k , g =( R̄ m −1 , 0 ) for R k , g =( R̄ m , 0 ) and S k , g =( R̄ m −1 , 0 ) in Eq (1), we obtain the structural nested multiplicative survival time model (SNMSTM) considered by Martinussen et al (2011, p. 776). We chose to fit an SNCFTM rather than a SNMSTM because, with rare failures, unconstrained estimation of an SNMSTM may result in estimated survival probabilities exceeding 1.…”

Section: Structural Nested Cumulative Failure Time Modelsmentioning

confidence: 99%

Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions

Picciotto

Hernán

Page

et al. 2012

Journal of the American Statistical Association

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In the presence of time-varying confounders affected by prior treatment, standard statistical methods for failure time analysis may be biased. Methods that correctly adjust for this type of covariate include the parametric g-formula, inverse probability weighted estimation of marginal structural Cox proportional hazards models, and g-estimation of structural nested accelerated failure time models. In this article, we propose a novel method to estimate the causal effect of a time-dependent treatment on failure in the presence of informative right-censoring and time-dependent confounders that may be affected by past treatment: g-estimation of structural nested cumulative failure time models (SNCFTMs). An SNCFTM considers the conditional effect of a final treatment at time m on the outcome at each later time k by modeling the ratio of two counterfactual cumulative risks at time k under treatment regimes that differ only at time m. Inverse probability weights are used to adjust for informative censoring. We also present a procedure that, under certain “no-interaction” conditions, uses the g-estimates of the model parameters to calculate unconditional cumulative risks under nondynamic (static) treatment regimes. The procedure is illustrated with an example using data from a longitudinal cohort study, in which the “treatments” are healthy behaviors and the outcome is coronary heart disease.

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Estimation of Direct Effects for Survival Data by using the Aalen Additive Hazards Model

Cited by 44 publications

References 36 publications

Mediation Analysis: A Practitioner's Guide

Mediation Analysis: A Practitioner's Guide

Causality, Mediation and Time: A Dynamic Viewpoint

Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions

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