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Discussion on “Causal mediation of semicompeting risks” by Yen‐Tsung Huang

Author

Listed:
  • Isabel R. Fulcher
  • Ilya Shpitser
  • Vanessa Didelez
  • Kali Zhou
  • Daniel O. Scharfstein

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.

Suggested Citation

  • Isabel R. Fulcher & Ilya Shpitser & Vanessa Didelez & Kali Zhou & Daniel O. Scharfstein, 2021. "Discussion on “Causal mediation of semicompeting risks” by Yen‐Tsung Huang," Biometrics, The International Biometric Society, vol. 77(4), pages 1165-1169, December.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:4:p:1165-1169
    DOI: 10.1111/biom.13519
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    References listed on IDEAS

    as
    1. Vanessa Didelez, 2019. "Defining causal mediation with a longitudinal mediator and a survival outcome," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 593-610, October.
    2. Kosuke Imai & Dustin Tingley & Teppei Yamamoto, 2013. "Experimental designs for identifying causal mechanisms," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(1), pages 5-51, January.
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