Examining moderated effects of additional adolescent substance use treatment: Structural nested mean model estimation using inverse-weighted regression-with-residuals

RAND Statistics Seminar Series

Examining moderated effects of additional adolescent substance use treatment: Structural nested mean model estimation using inverse-weighted regression-with-residuals

Presented by Dr. Daniel Almirall—Survey Research Center in the Institute for Social Research, University of Michigan

Thursday, March 22nd, 2012
1:30 p.m. – 3:00 p.m. ET
Conference Room 6202
RAND Corporation, Pittsburgh, CA

Please contact Fabiola Lopez if you would like to attend this seminar.

Abstract

An effect moderator is a measure which tempers, specifies, or alters the effect of treatment. Moderators can be used to explain the heterogeneity of treatment (exposure) effects. In clinical and public health practice, they are often the basis for individualizing treatment. This talk considers the methodological problem of assessing effect moderation using data arising from non-experimental, longitudinal studies in which treatment is time-varying and so are the covariates thought to moderate its effect.

The talk is motivated by a longitudinal data set of 2870 adolescent substance users who are followed over the course of one year, with measurement occasions at baseline/intake and every 3 months thereafter. Treatment receipt and substance use frequency over the past 3 months, and a large number of other covariates are measured at each occasion. Using this data set, we examine the moderated time-varying effects of additional adolescent substance use treatment on future substance use, conditional on past time-varying frequency of use (the candidate time-varying moderator).

We employ a Structural Nested Mean Model (SNMM; Robins, 1994) to formalize the moderated time-varying causal effects of interest. We present an easy-to-use estimator of the SNMM which combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. In previous work (Almirall, Ten Have, Murphy 2010; Almirall, McCaffrey, Ramchand, Murphy 2011), we discuss how the RR approach identifies the moderated time-varying effects if the candidate time-varying moderators are the sole time-varying confounders. The combined RR+IPTW approach identifies the moderated time-varying effects in the presence of an additional, auxiliary set of known and measured putative time-varying confounders, which are not candidate time-varying moderators of scientific interest. (In the substance use example, this auxiliary set of covariates is large.) Further, we discuss problems with the traditional regression estimator, clarify the distinction between time-varying effect moderation vs time-varying confounding, and, if time permits, we discuss commonalities and differences between the (more commonly used) Marginal Structural Model (MSM; Robins, Hernan, Brumback 2000) and the SNMM.

Speaker Bio

Dr. Almirall is a Faculty Research Fellow in the Survey Research Center in the Institute for Social Research, University of Michigan. An applied statistician by training, Daniel has research interests in two related areas: First, he interested in the development and application of methods for causal inference using longitudinal data sets in which treatments (or exposures) of interest, covariates, and outcomes are all time-varying. His talk at RAND is in this area on the topic of time-varying effect moderation. Second, he is interested in the development and application of methods and experimental study designs (such as the sequential multiple assignment randomized trial) used to form individually-tailored adaptive health interventions. He is particularly interested in the substantive areas of mental health and substance use (especially as related to children and adolescents). Daniel was a summer associate at RAND in 2004.

Attending a Seminar

RAND visitors are welcome to attend and must RSVP at least one day prior to the seminar. To ensure your attendance please contact Fabiola Lopez at flopez@rand.org with your name, company (or university) affiliation, and national citizenship (for security purposes).

For parking and directions to RAND's Pittsburgh office, please see: http://www.rand.org/about/locations/pittsburgh.html.

For further information and to be added to the mailing list contact Fabioloa Lopez at flopez@rand.org.