Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Cheng finds marriage may not be best career option for women

Lam discusses youth population dynamics and economics in sub-Saharan Africa

Work by Bailey and Dynarski cited in NYT piece on income inequality

Highlights

Jeff Morenoff makes Reuters' Highly Cited Researchers list for 2014

Susan Murphy named Distinguished University Professor

Sarah Burgard and former PSC trainee Jennifer Ailshire win ASA award for paper

James Jackson to be appointed to NSF's National Science Board

Next Brown Bag


PSC Brown Bags will return in the fall

Structural Nested Mean Models for Assessing Time-Varying Effect Moderation

Publication Abstract

Almirall, Daniel, Thomas Ten Have, and Susan A. Murphy. 2010. "Structural Nested Mean Models for Assessing Time-Varying Effect Moderation." Biometrics, 66(1): 131-139.

This article considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time varying and so are the covariates said to moderate its effect. Intermediate causal effects that describe time-varying causal effects of treatment conditional on past covariate history are introduced and considered as part of Robins' structural nested mean model. Two estimators of the intermediate causal effects, and their standard errors, are presented and discussed: The first is a proposed two-stage regression estimator. The second is Robins' G-estimator. The results of a small simulation study that begins to shed light on the small versus large sample performance of the estimators, and on the bias-variance trade-off between the two estimators are presented. The methodology is illustrated using longitudinal data from a depression study.

DOI:10.1111/j.1541-0420.2009.01238.x (Full Text)

PMCID: PMC2875310. (Pub Med Central)

Country of focus: United States.

Browse | Search : All Pubs | Next