Causal mediation analyses for randomized trials

  • Kevin G. Lynch
  • Mark Cary
  • Robert Gallop
  • Thomas R. Ten Have
Article

DOI: 10.1007/s10742-008-0028-9

Cite this article as:
Lynch, K.G., Cary, M., Gallop, R. et al. Health Serv Outcomes Res Method (2008) 8: 57. doi:10.1007/s10742-008-0028-9

Abstract

In the context of randomized intervention trials, we describe causal methods for analyzing how post-randomization factors constitute the process through which randomized baseline interventions act on outcomes. Traditionally, such mediation analyses have been undertaken with great caution, because they assume that the mediating factor is also randomly assigned to individuals in addition to the randomized baseline intervention (i.e., sequential ignorability). Because the mediating factors are typically not randomized, such analyses are unprotected from unmeasured confounders that may lead to biased inference. We review several causal approaches that attempt to reduce such bias without assuming that the mediating factor is randomized. However, these causal approaches require certain interaction assumptions that may be assessed if there is enough treatment heterogeneity with respect to the mediator. We describe available estimation procedures in the context of several examples from the literature and provide resources for software code.

Keywords

Structural mean models Principal stratification Direct effects Unmeasured confounding Baseline randomization Sequential ignorability 

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Kevin G. Lynch
    • 1
  • Mark Cary
    • 2
  • Robert Gallop
    • 2
  • Thomas R. Ten Have
    • 2
  1. 1.Department of PsychiatryUniversity of Pennsylvania School of MedicinePhiladelphiaUSA
  2. 2.Department of Biostatistics and EpidemiologyUniversity of Pennsylvania School of MedicinePhiladelphiaUSA

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