Literature DB >> 33783395

Causal Organic Indirect and Direct Effects: Closer to the Original Approach to Mediation Analysis, with a Product Method for Binary Mediators.

Judith J Lok1, Ronald J Bosch2.   

Abstract

Mediation analysis, which started in the mid-1980s, is used extensively by applied researchers. Indirect and direct effects are the part of a treatment effect that is mediated by a covariate and the part that is not. Subsequent work on natural indirect and direct effects provides a formal causal interpretation, based on cross-worlds counterfactuals: outcomes under treatment with the mediator set to its value without treatment. Organic indirect and direct effects avoid cross-worlds counterfactuals, using so-called organic interventions on the mediator while keeping the initial treatment fixed at treatment. Organic indirect and direct effects apply also to settings where the mediator cannot be set. In linear models where the outcome model does not have treatment-mediator interaction, both organic and natural indirect and direct effects lead to the same estimators as in the original formulation of mediation analysis. Here, we generalize organic interventions on the mediator to include interventions combined with the initial treatment fixed at no treatment. We show that the product method holds in linear models for organic indirect and direct effects relative to no treatment even if there is treatment-mediator interaction. Moreover, we find a product method for binary mediators. Furthermore, we argue that the organic indirect effect relative to no treatment is very relevant for drug development. We illustrate the benefits of our approach by estimating the organic indirect effect of curative HIV treatments mediated by two HIV persistence measures, using data on interruption of antiretroviral therapy without curative HIV treatments combined with an estimated or hypothesized effect of the curative HIV treatments on these mediators. See video abstract at http://links.lww.com/EDE/B796.
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Entities:  

Mesh:

Year:  2021        PMID: 33783395      PMCID: PMC8362675          DOI: 10.1097/EDE.0000000000001339

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  31 in total

1.  Can we avoid treatment interruption studies in the search for an HIV cure?

Authors:  Jade Ghosn; Constance Delaugerre
Journal:  AIDS       Date:  2015-07-31       Impact factor: 4.177

2.  Identifiability and exchangeability for direct and indirect effects.

Authors:  J M Robins; S Greenland
Journal:  Epidemiology       Date:  1992-03       Impact factor: 4.822

3.  The consistency statement in causal inference: a definition or an assumption?

Authors:  Stephen R Cole; Constantine E Frangakis
Journal:  Epidemiology       Date:  2009-01       Impact factor: 4.822

4.  Defining and estimating causal direct and indirect effects when setting the mediator to specific values is not feasible.

Authors:  Judith J Lok
Journal:  Stat Med       Date:  2016-05-26       Impact factor: 2.373

5.  Maternal viral load, zidovudine treatment, and the risk of transmission of human immunodeficiency virus type 1 from mother to infant. Pediatric AIDS Clinical Trials Group Protocol 076 Study Group.

Authors:  R S Sperling; D E Shapiro; R W Coombs; J A Todd; S A Herman; G D McSherry; M J O'Sullivan; R B Van Dyke; E Jimenez; C Rouzioux; P M Flynn; J L Sullivan
Journal:  N Engl J Med       Date:  1996-11-28       Impact factor: 91.245

Review 6.  Clinical biomarkers in drug discovery and development.

Authors:  Richard Frank; Richard Hargreaves
Journal:  Nat Rev Drug Discov       Date:  2003-07       Impact factor: 84.694

Review 7.  Latency reversal and viral clearance to cure HIV-1.

Authors:  David M Margolis; J Victor Garcia; Daria J Hazuda; Barton F Haynes
Journal:  Science       Date:  2016-07-22       Impact factor: 47.728

8.  Interleukin-2 therapy in patients with HIV infection.

Authors:  D Abrams; Y Lévy; M H Losso; A Babiker; G Collins; D A Cooper; J Darbyshire; S Emery; L Fox; F Gordin; H C Lane; J D Lundgren; R Mitsuyasu; J D Neaton; A Phillips; J P Routy; G Tambussi; D Wentworth
Journal:  N Engl J Med       Date:  2009-10-15       Impact factor: 91.245

9.  The size of the expressed HIV reservoir predicts timing of viral rebound after treatment interruption.

Authors:  Jonathan Z Li; Behzad Etemad; Hayat Ahmed; Evgenia Aga; Ronald J Bosch; John W Mellors; Daniel R Kuritzkes; Michael M Lederman; Michael Para; Rajesh T Gandhi
Journal:  AIDS       Date:  2016-01-28       Impact factor: 4.177

10.  CD4+ count-guided interruption of antiretroviral treatment.

Authors:  W M El-Sadr; J D Lundgren; J D Neaton; F Gordin; D Abrams; R C Arduino; A Babiker; W Burman; N Clumeck; C J Cohen; D Cohn; D Cooper; J Darbyshire; S Emery; G Fätkenheuer; B Gazzard; B Grund; J Hoy; K Klingman; M Losso; N Markowitz; J Neuhaus; A Phillips; C Rappoport
Journal:  N Engl J Med       Date:  2006-11-30       Impact factor: 91.245

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.