We are excited to announce a new series of online workshops. Our 1 & 1 workshops will consist of a one-hour live video presentation on a method followed by a one-hour question-and-answer session with the presenter (and possibly other experts). For our first 1 & 1, Methodology Center Scientific Director Stephanie Lanza will present an introduction to time-varying effect modeling (TVEM) on Tuesday, February 21st, from 3:00 to 5:00 p.m. Eastern Standard Time. After the presentation, she will accept questions via instant message and will answer them through the video connection. This is a great opportunity to learn about the value of TVEM and ask questions to one of the pioneers of applying TVEM to health and behavioral data.
Methodology Center Collaborative Research Projects are designed to bring talented and energetic researchers into the field of methodology and to foster new collaborative research ties between Methodology Center researchers and other faculty at Penn State. We are seeking competitive proposals to develop methodology in the prevention and treatment of substance use, HIV, or related problems, but prior experience in these research areas is not required. We encourage proposals from quantitatively oriented researchers from any discipline who can bring exciting, new perspectives to the study of prevention and treatment.
December 2, 2016
Latent class analysis (LCA) is a widely used tool for identifying subgroups in a population. Many researchers have questions about how to conduct an LCA as responsibly and accurately as possible. In our latest podcast, John Dziak discusses important points to consider when conducting an LCA, like how to tell when an analysis is successful and how to make sure your model is properly identified. John is a Methodology Center research associate who studies LCA, and he is the lead developer of our LCA software, including PROC LCA. Note: this podcast is a companion piece to podcasts 15 and 16 with Stephanie Lanza and Bethany Bray. If you are new to LCA, you may want to start with Podcast 15.
December 1, 2016
A common fear of many smokers who want to quit is that they will lose many people in their social network―family, friends or co-workers―when they quit. In a recent article in the journal Nicotine and Tobacco Research, researchers applied latent transition analysis to examine the changes in the social networks of smokers who are quitting. The authors identified five types of networks and found that people who successfully quit are actually likely to increase the size of their social networks.
November 30, 2016
Micro-randomized trials (MRTs) are a type of experiment for use in developing a mobile intervention. In order to understand MRTs, consider an intervention that promotes physical activity among cardiac patients.
A new web applet allows users to calculate the number of subjects needed for an MRT given the length of the study, the number of randomizations per day, and a few other criteria. The methodological foundation of the applet is explained in "Sample size calculations for micro-randomized trials in mHealth,"recently published in the journal Statistics in Medicine.
Open the article. (Journal access required.)
October 31, 2016
Over the course of treatment, a clinician often alters treatment based on patient characteristics or response to earlier treatment. Sequential, multiple assignment, randomized trial (SMART) designs provide the data needed to construct high-quality adaptive interventions. Interventions that adapt at the right times (e.g., intensifying for people who do not respond to the initial treatment) can improve participant outcomes while decreasing the cost and burden of the intervention (e.g., stepping down treatment for responsive participants). SMART designs are currently being used around the world in dozens of trials to build adaptive interventions for drug use, HIV, ADHD, autism, obesity, and more.
Last year, a team of Methodology Center researchers was awarded a grant from the National Institute on Drug Abuse (R01 DA039901) to expand the methodological toolbox available for intervention designers seeking to analyze data and plan future SMART studies.
October 27, 2016
There is an opening for a post-doctoral researcher as part of the Prevention and Methodology Training (PAMT) program. PAMT is an interdisciplinary program, and trainees receive mentorship from experts in both The Bennett Pierce Prevention Research Center and The Methodology Center in Penn State's College of Health and Human Development. Postdocs will be immediately involved in ongoing projects with opportunities to publish. They are also encouraged to develop their own lines of research, with mentoring on how to do so and protected time to make progress.
October 25, 2016
Research indicates that withdrawal is one of the primary reasons that people do not quit smoking (Piper, 2015). Improving our understanding of withdrawal may allow us to better support people who wish to quit smoking. In a new article in Addiction, "What a difference a day makes: Differences in initial abstinence response during a smoking cessation attempt," the authors present a latent class analysis (LCA) that identifies four types of smokers based on their withdrawal symptoms on the day they quit. They found that a subset of quitting smokers reported extreme craving or extreme negative affect, and that this predicted earlier relapse.
October 25, 2016
We recently released a nine-minute video that provides a conceptual overview of time-varying effect modeling (TVEM). Methodology Center Scientific Director Stephanie Lanza describes two published examples of TVEM and explains how TVEM can be useful with different types of data. This is the second in our new series of instructional videos; a video introduction to latent class analysis is also available.
September 23, 2016
Congratulations to Eric Laber, associate professor of statistics at North Carolina State University, recipient of The Methodology Center 2016 Distinguished Alumni Award. Eric develops methods for data-driven decision making. He applies his work in a broad variety of ways including precision medicine, artificial intelligence, adaptive conservation, and the management of infectious diseases.