Issue: October 2013
eNews from The Methodology Center
Susan Murphy Receives MacArthur 'Genius' Award Methodology Center Principal Investigator Susan Murphy was named a MacArthur fellow last week. Susan is one of 24 people from all walks of life -- choreographers, physicists, a pianist, a physician, and a paleobotanist to name a few -- to be recognized this year. Susan is being honored for her work developing the sequential, multiple assignment, randomized trial (SMART). SMART designs provide scientists with the empirical tools to build adaptive interventions: treatment rules that dictate whether, how, and when to alter treatment for patients. The National Institutes of Health are currently funding SMART designs to build better treatments for a broad range of health problems including cocaine abuse, depression, alcohol abuse, ADHD, autism, and bipolar disorder.

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Watch Susan discuss the award


Workshop on Methodology for HIV: Free Materials The Penn State Methodology Center, in collaboration with Columbia University's School of Social Work, organized a two-day meeting between experts in HIV prevention and experts in methods for engineering better behavioral interventions on September 12 and 13 in Bethesda, Maryland. The meeting gave methodologists insight into the state of HIV research and the challenges faced by HIV researchers. The HIV researchers were introduced to new methods for experimental design, specifically the multiphase optimization strategy (MOST) and the sequential, multiple assignment, randomized trial (SMART). Videos of the keynote addresses by Seth Kalichman and Rick Altice and slides from all presentations are available below.  


View presentations and videos.



Latent class analysis (LCA) has become a popular tool for identifying subgroups within populations. Despite the fact that these are latent subgroups and, therefore, membership in each class is unknown, research questions sometimes make it necessary to assign individuals to classes and then treat class membership as known in later analyses. Current standard practice is to retain from LCA each individual's posterior probabilities of class membership, and then either assign them to the most likely class or to repeatedly assign them using pseudo-class draws. These practices, however, are known to attenuate estimates in subsequent analyses.


In a new paper by Center Investigators Bethany Bray and Stephanie Lanza and Xianming Tan of McGill University, the authors propose a straightforward solution that includes all variables used in the subsequent analyses as covariates in the LCA. Then, class assignment is carried out using either the most likely class or pseudo-class draws. With adequate measurement quality and sample size, this approach substantially reduces the bias that results from common approaches to class assignment.


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The boom in mobile devices like smartphones has created new possibilities for treating a broad spectrum of health behaviors. An initiative at the University of Michigan involving several Methodology Center researchers is dedicated to developing learning algorithms, statistical analyses, and clinical trial designs to make the best use of mobile technologies to deliver personalized behavioral interventions. These interventions are known as just-in-time, adaptive interventions (JITAIs). In JITAIs, real-time data is used to inform the real-time delivery of an intervention. Similar to the adaptive interventions developed using sequential, multiple assignment randomized trials (SMARTs), JITAIs employ decision rules that link the data about the patient to subsequent treatment options. Unlike standard adaptive interventions, however, JITAIs are able to provide patients with immediate treatment that is salient to their specific circumstances. JITAIs are able to learn and re-learn, in real-time, how best to adapt treatment for the individual patient.


Read more.


New Grant Uses TVEM to Understand Opioid Withdrawal

Part of the danger of opioids like oxycodone, morphine, and heroin stems from their highly addictive nature. In a new project funded by the National Institute on Drug Abuse, Methodology Center Investigator Stephanie Lanza will collaborate with Principal Investigators Roger Meyer and Scott Bunce and their team from Penn State's Milton S. Hershey Medical Center to improve treatment for patients recovering from opioid dependence. By following patients for seven months, researchers will track changes in the patients' brain reward systems from initial detoxification through residential care and a follow-up period. The researchers will use time-varying effect models to better understand changes in mood, stress level, craving, and sleep. By studying these processes through initial recovery from dependence, researchers will learn how chemical balances in the brain return to levels typically found in people who are not dependent on opioids.


Read more in NIH Reporter.



New Grant on Weight Control During Pregnancy A new grant received by Principal Investigator Danielle Symons Downs will work to reduce gestational weight gain among overweight and obese pregnant women. Methodology Center Director Linda Collins is a collaborator on this project, which will develop an individually tailored behavioral intervention to manage gestational weight gain.


Read more.




New Web Page: Practice Decision-Making Data Sets for Factorial Experiments Often in the multiphase optimization strategy (MOST), factorial experiments are used for component screening. The results of the experiment form the basis for making decisions about what to include in the optimized intervention. Linda Collins and her collaborators have created practice data sets to help researchers prepare to analyze real data.


Open the practice data sets.




Want to learn more about one of our methods? We maintain a recommended reading list on causal inference and on all of our research projects. 


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