July 11, 2017
Secondary data analysis is a high priority for many funding agencies as they try to maximize the information gleaned from funded studies. In this podcast, Methodology Center Research Associate Kate Guastaferro and Methodology Center Data Manager Loren Masters discuss some of the issues and requirements associated with getting access to existing data. This podcast is intended for graduate students or investigators who are new to secondary data analysis. Along with the podcast, users can download an outline of the steps required before conducting a secondary data analysis.
July 11, 2017
June 16, 2017
Our LCA distal outcome estimation software allows users to estimate the association between a latent class variable and a distal outcome in either Stata or SAS. Our previous LCA distal outcome releases have employed a model-based approach known as the LTB approach. As research on LCA with a distal outcome advanced, it became clear that a different method, one developed by Bolck, Croon and Hagenaars (2004), produces more accurate estimates in certain cases. We have implemented this method, known as the BCH approach, in a new SAS macro and Stata function. We recommend the new LCA_Distal_BCH SAS macro and LCA_Distal_BCH Stata function for estimating LCA with a distal outcome.
The Methodology Center is pleased to announce the availability of a new web applet for calculating the minimum sample size for a pilot SMART. The sequential, multiple assignment, randomized trial (SMART) is a novel experimental design that can be used to build high quality adaptive interventions that adapt to patient need. Pilot SMARTs can be used to examine feasibility and acceptability issues of adaptive interventions embedded in a full-scale SMART study.
April 18, 2017
Join us at the Society for Prevention Research (SPR) 2017 Annual Meeting, May 30 through June 2 in Washington, D.C. Methodology Center researchers will present symposia, talks, posters, special interest groups, and a roundtable on a broad array of topics including youth substance abuse, HIV-risk behavior, optimization of interventions, sexual abuse prevention, health disparities for sexual minorities, and much more. Of special note, SPR is launching a new task force focused on big data in prevention research, and Mildred Maldonado-Molina and Stephanie Lanza are hosting a roundtable about the initiative on Thursday morning.
April 12, 2017
We are pleased to release the newest extension of our TVEM (time-varying effect model) software. The %WeightedTVEM SAS macro (version 2.6) fits TVEMs on complex datasets that involve clustering (e.g., students are nested within schools) and survey weights (e.g., participants represent different numbers of population members due to systematically unequal probabilities of selection). Before attempting to use %WeightedTVEM, users should familiarize themselves with the %TVEM SAS macro (version 3.1 or higher).
April 6, 2017
Response to substance abuse treatment can look very different between individuals and even within individuals at different points in time. Sequential, multiple assignment, randomized trials (SMARTs) are being used to develop interventions that adapt based on individual needs and circumstances. New methods for data analysis show promise for improving intervention developers’ ability to tailor an intervention even more specifically to an in individual's needs for a broad range of health issues, including substance use. In a recent article in the journal Addiction, Methodology Center researchers Inbal (Billie) Nahum-Shani, Daniel Almirall, and their collaborators demonstrate the utility of Q-learning, a method developed in computer science, for the analysis of data from a SMART to prevent relapse among individuals with alcohol use disorders. Q-learning helped the authors identify a subset of individuals who appeared to be responding to treatment, but who needed additional treatment to maintain progress.
March 13, 2017
In our latest video releases, Methodology Center Investigator Susan Murphy introduces some innovative tools for building adaptive health interventions that can be delivered through a smartphone or other mobile device. In the first video, she introduces the just-in-time adaptive intervention (JITAI), a type of intervention that uses real-time data to deliver interventions as needed via mobile devices. In the second video, she introduces the microrandomized trial, an innovative trial design for building JITAIs. In the third video, Susan discusses data analysis to inform the development of a JITAI.
Photo credit: John D. and Catherine T. MacArthur Foundation
March 9, 2017
As state laws regarding marijuana change around the nation, legislators and the public need information about the impacts of marijuana use. Research has shown that smoking marijuana in order to cope with problems is associated with later marijuana-related problems (Fox et al., 2011). In a recent article in Journal of Studies on Alcohol and Drugs, a team of researchers including Methodology Center Associate Director Bethany Bray examined data on self-reported motives for using marijuana during young adulthood and then determined which motivational profiles were associated with later marijuana use and problems.
In our latest podcast, Methodology Center Research Associate Michael Russell discusses ambulatory assessment and his pilot project examining self-report data during heavy drinking. In the project, Michael is combining ecological momentary assessment (EMA) of self-reported alcohol use with continuous data from ankle bracelets that measure alcohol intoxication levels through contact with the skin. He is investigating the accuracy of using EMA self-reports as a proxy for such intoxication measures during real-world drinking episodes. He discusses his thoughts on the challenges and opportunities of such data collection, and talks about some of his research using these and other intensive longitudinal data (ILD).
January 18, 2017
Every year in the United States, 800,000 deaths are directly attributable to behavioral factors like smoking and alcohol use. Interventions that help people modify their risky behavior could save many lives. Because adaptive interventions (also called dynamic treatment regimens) adjust based on participant need or preference, they have the capacity to increase intervention effectiveness and/or decrease cost and patient burden.