Featured Articles | The Methodology Center

Featured Articles

woman breaking cigaretteApril 29, 2015

Year of TVEM

In 2014, a special issue of Nicotine and Tobacco Research featured applications of the time-varying effect model (TVEM) and other methods for the analysis of intensive longitudinal data (ILD). In that issue, four NIH officials—Elizabeth Ginexi, William Riley, and Audie Atienza of the National Cancer Institute (NCI), and Patricia Mabry of the Office of Behavioral and Social Sciences Research (OBSSR)—authored an editorial, “The promise of intensive longitudinal data capture for behavioral health research.” In this piece, the authors discussed the potential of ILD to advance science and highlighted resources at NIH for supporting research related to the collection and analysis of ILD.


March 8, 2015

Melissa Boone, Ph.D.Unprotected sexual intercourse among HIV-positive men who have sex with men (MSM) puts them at risk for sexually transmitted infections and their partners at risk for HIV. Therefore, it is important to understand factors that lead to sexual risk among HIV-positive MSM. A recent article examined how changes over time in a person’s sense of well-being can influence their sexual risk behavior. The article, “Fluctuations in Depression and Well-Being Are Associated With Sexual Risk Episodes Among HIV-Positive Men,” was authored by Prevention and Methodology Training (PAMT) postdoctoral fellow Melissa Boone and a research group at Columbia University. In it, the authors analyzed intensive longitudinal data collected from 106 sexually active, HIV-positive MSM.

January 27, 2015

child using a tablet

Adaptive interventions help guide clinicians in their decisions concerning when and how treatments should be altered, but developing empirically based adaptations requires gathering the right kind of data. The sequential, multiple assignment, randomized trial is a recent innovation that can provide high-quality, experimental data for developing adaptive interventions. Recently, a group of autism researchers published the results of their SMART study in the article “Communication interventions for minimally verbal children with autism: A sequential, multiple assignment, randomized trial,” which appears in the Journal of the American Academy of Child and Adolescent Psychiatry, a top journal in child and adolescent mental health. The authors, led by Connie Kasari of UCLA, designed a project to improve spoken communication for children with autism who are minimally verbal. The study’s results show the benefit of integrating speech-generating devices (SGD) as a part of language development interventions and the potential of SMART designs for developing adaptive interventions.


December 3, 2014

Rebecca Evans-PolceIn the United States, rates of substance use peak during adolescence and young adulthood. Previous literature has demonstrated that rates differ by race, ethnicity, and gender. Despite knowledge of these disparities, until now researchers have been unable to understand the extent to which these disparities change across adolescence and young adulthood. In the forthcoming article, “Changes in gender and racial/ethnic disparities in rates of cigarette use, regular heavy episodic drinking, and marijuana use: Ages 14 to 32,” to appear in Addictive Behaviors, Methodology Center researchers Rebecca Evans-Polce, Sara Vasilenko, and Stephanie Lanza use the time-varying effect model (TVEM) to examine the dynamic nature of substance use rates among different groups of adolescents and young adults.


November 10, 2014

Donna CoffmanPrevious literature has established that gang membership is associated with higher rates of drug use. In the forthcoming article, “Gang membership and substance use: Guilt as a gendered causal pathway,” in The Journal of Experimental Criminology, Methodology Center Investigator Donna Coffman and two other researchers examine whether anticipated guilt for substance use explains this association. The authors also expand the available set of methods for causal inference when assessing mediation in the presence of moderation and time-varying confounding.  

September 25, 2014
classroom with kids and teacherHead Start is the largest federally funded preschool program for disadvantaged children. Some people have questioned its worth because research has shown relatively small impacts on cognitive and social skills. In a forthcoming article in Child Development, Methodology Center Affiliate Brittany Rhoades Cooper and Methodology Center Scientific Director Stephanie Lanza perform latent class analysis (LCA) on the Head Start Impact Study dataset to identify subgroups of children who have similar home environments and caregivers.


The authors then examine cognitive, behavioral, and relationship skills measured two years later to determine whether the effects of Head Start vary across these subgroups. Their approach could have broad application by researchers interested in using latent class analysis to understand differential effects of an intervention by simultaneously considering multiple potential moderators. 


Donna Coffman

April 3, 2014

When parents know more about their teenage children’s activities, those children are less likely to engage in risky behavior (e.g., delinquency, substance use initiation). Though this connection is widely acknowledged, it is not clear whether the knowledge is the determining factor in the reduced risk, because many other factors are involved in the parent-child relationship. In a recent article in Prevention Science, the authors use propensity scores to examine the causal nature of this relationship. The research team includes former Penn State graduate student Melissa Lippold, Methodology Center Principal Investigator Donna Coffman, and Edna Peterson Bennett Endowed Chair in Prevention Research Mark Greenberg. 

Constantino LagoaMarch 5, 2014

In control engineering, devices continually monitor the performance of a system and then operate to control that system. Common examples include automobile thermostats and autopilot systems on commercial airliners. These same principles can be used to design behavioral interventions that adapt over time to help patients alter behaviors that affect their health. Potential applications include maintaining an exercise regimen, maintaining a healthy diet, or abstaining from tobacco or illicit drug use.


In a forthcoming article in the Journal of Consulting and Clinical Psychology, Methodology Center Investigators Constantino Lagoa, Stephanie Lanza, Susan Murphy, and their colleague Korkut Bekiroglu describe this new approach to building adaptive, intensive interventions. The authors show how data, in this case simulated smoking-cessation data, can be used to inform the design of an adaptive, intensive intervention by applying control-engineering techniques. This intervention, which is designed to provide treatment only when needed, is shown to improve effectiveness while decreasing patient burden. This approach holds great promise for informing clinical decisions and for informing the development of smartphone-based adaptive interventions. 

Linda CollinsDecember 10, 2013

The multiphase optimization strategy (MOST) provides a framework for building effective and efficacious behavioral interventions by following principles from the field of engineering. MOST emphasizes efficiency and resource management to move intervention science forward, and one of the cornerstones of MOST is selecting the most efficient and appropriate design for each experiment. In many instances this means conducting a factorial experiment. (Read more about factorial experimental designs.) In a new article in Translational Behavioral Medicine, Linda Collins and her coauthors explore how to use data from a factorial screening experiment to decide what should be included in the experimental intervention treatment package. This article is aimed at scientists who are considering conducting a factorial screening experiment.

Will quitting smokers lapse?November 15, 2013

In an article in a forthcoming special issue of Nicotine and Tobacco Research, a team of Methodology Center scientists examines the changing relationships between factors that predict relapse for smokers who are quitting. The researchers, led by Research Associate Sara Vasilenko, used time-varying effect models to analyze ecological momentary assessment data and found that predictors of relapse changed over the two weeks post quit. Cigarette cravings significantly predicted relapse throughout the two weeks. However, baseline dependence was significant early in the quit process but was unimportant after one week. Conversely, negative mood became a stronger predictor of relapse as time progressed.

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