Abstract Details
Activity Number:
|
491
|
Type:
|
Contributed
|
Date/Time:
|
Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistics in Epidemiology
|
Abstract - #310210 |
Title:
|
Defining and Estimating Causal Direct and Indirect Effects: An Intervention-Based Approach
|
Author(s):
|
Judith J. Lok*+
|
Companies:
|
Harvard School of Public Health
|
Keywords:
|
mediation ;
organic direct and indirect effects ;
natural direct and indirect effects ;
post-treatment mediator-outcome confounding ;
randomized data ;
observational data
|
Abstract:
|
Natural direct and indirect effects decompose the effect of a treatment into the part that is mediated by a covariate (the mediator) and the part that is not. The definition of natural direct and indirect effects relies on cross-worlds quantities: the outcomes under treatment with the mediator 'set' to its value without treatment. How to set the mediator is usually unspecified, which in practice often renders these quantities undefined. This presentation introduces 'organic' direct and indirect effects, generalizations of natural direct and indirect effects, which can be defined, identified, and estimated without relying on the potential outcomes under all combinations of treatment and mediator. For example, only part of the effect of some treatments for HIV/AIDS on mother-to-child transmission of HIV-infection is mediated by the effect of the treatment on the HIV viral load in the blood of the mother. However, setting HIV viral load to specific values is not possible in practice. The newly defined organic direct and indirect effects are therefore more appropriate in such a setting than the well-known natural direct and indirect effects.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.