Literature DB >> 34510405

Discussion on "Causal mediation of semicompeting risks" by Yen-Tsung Huang.

Isabel R Fulcher1, Ilya Shpitser2, Vanessa Didelez3, Kali Zhou4, Daniel O Scharfstein5.   

Abstract

Huang proposes a method for assessing the impact of a point treatment on mortality either directly or mediated by occurrence of a nonterminal health event, based on data from a prospective cohort study in which the occurrence of the nonterminal health event may be preemptied by death but not vice versa. The author uses a causal mediation framework to formally define causal quantities known as natural (in)direct effects. The novelty consists of adapting these concepts to a continuous-time modeling framework based on counting processes. In an effort to posit "scientifically interpretable estimands," statistical and causal assumptions are introduced for identification. In this commentary, we argue that these assumptions are not only difficult to interpret and justify, but are also likely violated in the hepatitis B motivating example and other survival/time to event settings as well.
© 2021 The International Biometric Society.

Entities:  

Keywords:  causal inference; mediation; semicompeting risks; survival analysis

Mesh:

Year:  2021        PMID: 34510405     DOI: 10.1111/biom.13519

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  2 in total

1.  Translating questions to estimands in randomized clinical trials with intercurrent events.

Authors:  Mats J Stensrud; Oliver Dukes
Journal:  Stat Med       Date:  2022-05-16       Impact factor: 2.497

2.  Methods of analysis for survival outcomes with time-updated mediators, with application to longitudinal disease registry data.

Authors:  Kamaryn T Tanner; Linda D Sharples; Rhian M Daniel; Ruth H Keogh
Journal:  Stat Methods Med Res       Date:  2022-06-16       Impact factor: 2.494

  2 in total

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