Mediation analysis with time varying exposures and mediators

• 成果类型: Article
• 署名作者: VanderWeele, Tyler J.; Tchetgen, Eric J. Tchetgen
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12194
• 发表日期: 2017
• 页码: 917-938
• 关键词: MARGINAL STRUCTURAL MODELS; Causal Inference; identification; Loneliness; LIFE

摘要

We consider causal mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are time varying confounders affected by prior exposure and a mediator, natural direct and indirect effects are not identified. However, we define a randomized interventional analogue of natural direct and indirect effects that are identified in this setting. The formula that identifies these effects we refer to as the mediational g-formula'. When there is no mediation, the mediational g-formula reduces to Robins's regular g-formula for longitudinal data. When there are no time varying confounders affected by prior exposure and mediator values, then the mediational g-formula reduces to a longitudinal version of Pearl's mediation formula. However, the mediational g-formula itself can accommodate both mediation and time varying confounders and constitutes a general approach to mediation analysis with time varying exposures and mediators.