{"doi":"10.1111/rssb.12194","title":"Mediation Analysis with time Varying Exposures and Mediators","abstract":"<jats:title>Summary</jats:title><jats:p>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.</jats:p>","journal":"Journal of the Royal Statistical Society Series B: Statistical Methodology","year":2017,"id":18511,"datarank":9.312769512388982,"base_score":5.652489180268651,"endowment":5.652489180268651,"self_citation_contribution":0.8478733770402977,"citation_network_contribution":8.464896135348685,"self_endowment_contribution":0.8478733770402977,"citer_contribution":8.464896135348685,"corpus_percentile":null,"corpus_rank":null,"citation_count":284,"citer_count":200,"citers_with_citation_signal":200,"citers_with_endowment":200,"datacite_reuse_total":22,"is_dataset":false,"is_dataset_confidence":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":128412,"name":"Eric J. Tchetgen Tchetgen","orcid":null,"position":1,"is_corresponding":false},{"id":122698,"name":"Tyler J. VanderWeele","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":5.652489180268651,"endowment":5.652489180268651,"datacite_reuse_total":22,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"28824285","pmcid":"PMC5560424","openalex_id":"https://openalex.org/W8423564","authors":[],"funders":[{"funder_name":"National Institutes of Health","grant_id":"ES017876","title":null},{"funder_name":"National Institutes of Health","grant_id":"AI104459","title":null},{"funder_name":"NIEHS NIH HHS","grant_id":"R56 ES017876","title":null},{"funder_name":"NIAID NIH HHS","grant_id":"R01 AI104459","title":null},{"funder_name":"NIEHS NIH HHS","grant_id":"R01 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