MULTI-LEVEL SURVIVAL MODELS FOR DRUG ABUSE PREVENTION DATA

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Many fundamentally important questions in theory-based research on substance use prevention have to so with a duration of time ot an event. For example, is a particular prevention program effective at delaying initial experimentation with alcohol? In this question the event is initial experimentation with alcohol. For another example, what is the relationship between onset of problem drinking and such factors as parental drinking and time of first experience with alcohol: The event here is onset of problem drinking. For various well-documented reasons, traditional linear regression techniques are inadequate in addressing these kinds of questions; instead, classical discrete time survival analysis is the appropriate statistical methodology. However, because much of classical survival analysis was developed for a clinical medical setting rather than a field research setting, rote application of classical survival analysis to prevention questions can lead to misleading results. This proposal deals with the modification of survival analysis methods to the unique circumstances of theory-based prevention/intervention research. In particular, classical survival analysis assumes that after adjusting for risk factors individuals are independent, yet this assumption is often violated in prevention data because of it s multi-level structure, e.g., students are grouped into classes, classes are grouped into schools and schools are grouped into school districts. A consequence of this multi- level structure is that students cannot be considered independent, because students in the same school can be expected to be more similar in substance use than students in different schools even after accounting for risk factors. The proposed research will develop survival analysis methods that recognize and take advantage of the multi-level structure of most drug abuse prevention and intervention data sets. These methods will be applied to prevention questions about risk, protective factors, vulnerability, and resiliency in data from the Adolescent Alcohol Prevention Trial.