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

PSC In The News

RSS Feed icon

Singh discusses her research in India on infertility

Johnston concerned declines in teen smoking threatened by e-cigarettes

Johnston says decreasing marijuana use among teens not easily explained

Highlights

Apply for 2-year NICHD Postdoctoral Fellowships that begin September 2015

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Michigan's graduate sociology program tied for 4th with Stanford in USN&WR rankings

Next Brown Bag

Monday, Jan 12
Filiz Garip, Changing Dynamics of Mexico-U.S. Migration

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 of America.

Browse | Search : All Pubs | Next