Corpus ID: 222090238

Defining and Estimating Subgroup Mediation Effects with Semi-Competing Risks Data

@article{Gao2020DefiningAE,
  title={Defining and Estimating Subgroup Mediation Effects with Semi-Competing Risks Data},
  author={Fei Gao and Fan Xia and Kwun Chuen Gary Chan},
  journal={arXiv: Methodology},
  year={2020}
}
In many medical studies, an ultimate failure event such as death is likely to be affected by the occurrence and timing of other intermediate clinical events. Both event times are subject to censoring by loss-to-follow-up but the nonterminal event may further be censored by the occurrence of the primary outcome, but not vice versa. To study the effect of an intervention on both events, the intermediate event may be viewed as a mediator, but conventional definition of direct and indirect effects… Expand

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References

SHOWING 1-10 OF 28 REFERENCES
Causal mediation of semicompeting risks.
TLDR
A nonparametric approach casting the semi-competing risks problem in the framework of causal mediation modeling is proposed, and its advantage in causal interpretation over existing methods is also demonstrated in a hepatitis study. Expand
Mediation analysis of time‐to‐event endpoints accounting for repeatedly measured mediators subject to time‐varying confounding
TLDR
This article will show how to identify and infer the path‐specific effect of treatment on the event time via the repeatedly measured mediator levels, and illustrate the method by an application to data from the LEADER cardiovascular outcomes trial. Expand
Survivor average causal effects for continuous time: a principal stratification approach to causal inference with semicompeting risks
In semicompeting risks problems, nonterminal time-to-event outcomes such as time to hospital readmission are subject to truncation by death. These settings are often modeled with illness-death modelsExpand
Time-dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model.
TLDR
This paper demonstrates that combining the g-formula with the additive hazards model and a sequential linear model for the mediator process results in simple and interpretable expressions for direct and indirect effects in terms of relative survival as well as cumulative hazards. Expand
Defining causal meditation with a longitudinal mediator and a survival outcome
TLDR
This work proposes and discusses an alternative definition of mediated effects that does not suffer from these problems, and is more transparent than the current alternatives, and gives assumptions allowing identifiability of such alternative mediated effects leading to the familiar mediation g-formula. Expand
Longitudinal Mediation Analysis with Time-varying Mediators and Exposures, with Application to Survival Outcomes
TLDR
This paper proposes a formulation in terms of random interventions based on conditional distributions for the mediator, which allows for well-defined natural direct and indirect effects in the survival setting, and natural decomposition of the standard total effect. Expand
Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders.
TLDR
The survival mediational g-formula constitutes a powerful tool for conducting mediation analysis with longitudinal data and is applied to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. Expand
Final Report of the Intergroup Randomized Study of Combined Androgen-Deprivation Therapy Plus Radiotherapy Versus Androgen-Deprivation Therapy Alone in Locally Advanced Prostate Cancer
  • M. Mason, W. Parulekar, +20 authors P. Warde
  • Medicine
  • Journal of clinical oncology : official journal of the American Society of Clinical Oncology
  • 2015
TLDR
The prespecified final analysis of this randomized trial demonstrates that the previously reported benefit in survival is maintained at a median follow-up of 8 years and firmly establishes the role of RT in the treatment of men with locally advanced prostate cancer. Expand
Semiparametric transformation models for causal inference in time to event studies with all-or-nothing compliance.
We consider causal inference in randomized survival studies with right censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution ofExpand
A simple unified approach for estimating natural direct and indirect effects.
TLDR
A simple procedure based on marginal structural models that directly parameterize the natural direct and indirect effects of interest is introduced and has the advantage that it can be conducted in standard software. Expand
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