Wang, et al., 2008). Besides its close collaboration with ongoing trials (Brown, Costigan, and Kendziora, 2008), PSMG has continued to maintain close ties to the developers of the Mplus statistical package, allowing for a seamless integration of new statistical models, broad application of these models in existing software, and application of these new methods in existing trials.
A similar interdisciplinary methodological group, the Methodology Center, is located at Pennsylvania State University and is funded by NIDA and the National Science Foundation. The Methodology Center works in collaboration with prevention and treatment researchers to advance and disseminate statistical methodology related to research on the prevention and treatment of problem behavior, particularly drug abuse. This group has developed longitudinal models that address the unique aspects of changes in drug use over time including latent transition analyses (Collins, Hyatt, and Graham, 2000; Chung, Park, and Lanza, 2005; Chung, Walls, and Park, 2007; Lanza, Collins, et al., 2005) and two-part growth models (Olsen and Schafer, 2001); missing data routines for large, longitudinal data sets (Schafer, 1997; Schafer and Graham, 2002; Demirtas and Schafer, 2003; Graham, 2003; Graham, Cumsille, and Elek-Fisk, 2003); designs and inferences that take into account varying dosages or levels of exposure to an intervention or adaptive interventions (Bierman, Nix, et al., 2006; Collins, Murphy, and Bierman, 2004; Collins, Murphy, and Strecher, 2007; Murphy, 2005; Murphy, Collins, and Rush, 2007; Murphy, Lynch, et al., 2007), and cost effectiveness (Foster, Porter, et al., 2007; Foster, Johnson-Shelton, and Taylor, 2007; Olchowski, Foster, and Webster-Stratton, 2007).
determines which participants are being examined, how and when they will be assessed, and what interventions they will receive; and (3) statistical analyses that model how those given an intervention differ on outcomes compared with those in a comparison condition. This chapter discusses statistical designs and analyses, as well as offering comments about measures and measurement systems. While there are important technical issues to consider for measurement, design, and analysis, the community and institutional partnerships that are necessary to create and carry out a mutually agreed-on agenda are critical to the development of quality prevention science (Kellam, 2000).
We discuss first the uses of randomized preventive trials, which have led to an extraordinary increase in knowledge about prevention programs (see Chapters 4 and 6). Because well-conducted randomized preventive trials produce high-quality conclusions about intervention effects, they have achieved a prominent place in the field of prevention research. Despite