Causal mediation analysis in the multilevel intervention and multicomponent mediator case

Cheng Zheng and Xiao-Hua Zhou

Journal of the Royal Statistical Society Series B, 2015, vol. 77, issue 3, 581-615

Abstract: type="main" xml:id="rssb12082-abs-0001">

Mediation analysis is an important tool in social and medical sciences as it helps to understand why an intervention works. The commonly used approach, given by Baron and Kenny, requires the strong assumption ‘sequential ignorability’ to yield causal interpretation. Ten Have and his colleagues proposed a rank preserving model to relax this assumption. However, the rank preserving model is restricted to the case with binary intervention and single mediator and needs another strong assumption ‘rank preserving’. We propose a new model that can relax this assumption and can handle both multilevel intervention and multicomponent mediators. As an estimating-equation-based method, our model can handle both correlated data with the generalized estimating equation and missing data with inverse probability weighting. Finally, our method can also be used in many other research settings, using mathematical models similar to mediation analysis, such as treatment compliance and post-randomized treatment component analysis. For the causal mediation model proposed, we first show identifiability for the parameters in the model. We then propose a semiparametric method for estimating the model parameters and derive asymptotic results for the estimators proposed. Simulation shows good performance for the proposed estimators in finite sample sizes. Finally, we apply the method proposed to two real world clinical studies: the college student drinking study, and the ‘Improving mood promoting access to collaborative treatment for late life depression’ study.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1111/rssb.2015.77.issue-3 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:77:y:2015:i:3:p:581-615

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868

Access Statistics for this article

Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom

More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().