Stage sequences play a major role is theory-based prevention research, where they have ben featured for years in models of the onset of use of individual substances and in models of multiple substance use onset. Latent Transition Analysis (LTA), a relatively new statistical methodology for estimating and testing stage-sequential models, has been developed as part of a previous and a current NIDA grant. LTA has been demonstrated to be highly useful in theory-based substance use prevention research, where it has provided a new, interesting, and uniquely revealing look at prevention data. The proposed research will develop and implement a major expansion of lTA, greatly broadening the usefulness of this procedure. In the proposed research we will add to LTA the capability of estimating and testing models where one stage-sequential process is used to predict another. Four different types of prediction will be implemented: (1) concomitant processes, where two stage sequential processes in separate domains tend to track along together over time (example: a target child and his or her peers tend to be ina the same stage of the onset process, even when stage membership changes over time); (2) lagged concomitant, where two stage sequences in separate domains tend to track along together over time, but the relation is separated by time (example: the stage peers are in at Time 1 coincides with the stage a target child is in at some specified later time); (3) dynamically concomitant, where the change in one stage- sequential process predicts the change in another (example: whenever there is a stage transition in peer use there is an accompanying stage transition in use by a target child, even when the target child and his or her peers are not in the same stage of the onset process); and (4) dynamically lagged, where the change in one sequence predicts change in the other sequence occurring after a period of time)example; stage transitions in peer use are followed by stage transitions in target child use a month later). Throughout the entire project, we will make use of the latest improvements to the LTA procedure to analyze existing substance use prevention data from Project AAPT. The AAPT of data will allow us the opportunity to use LTA to learn more about risk, protective factors, vulnerability, and resiliency in relation to changes in substance use behavior ina the years from age ten to young adulthood.
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