Intent-to-Treat and Postintervention Modeling

The traditional standard of intent-to-treat analyses used to analyze clinical trials has been extended to multilevel and growth modeling for randomized field trials. This approach overcomes the challenges in handling dropin and dropout and other types of missing data that regularly occur in prevention trials (Brown, Wang, et al., 2008). So-called intent-to-treat analyses, or analyses based on the assigned rather than the actual intervention or treatment, are generally used as the primary set of models to examine intervention effects overall and for moderating effects involving individual-level and group-level baseline characteristics.

These traditional methods of examining the effects of an intervention can be supplemented with postintervention analyses. The postintervention approach takes into account the intervention actually received by each participant (Wyman, Brown, et al., 2008), the dosage received (Murphy, 2005; Murphy, Collins, and Rush, 2007; Murphy, Lynch, et al., 2007), and the level of adherence (Little and Yau, 1996; Hirano, Imbens, et al., 2000; Barnard, Frangakis, et al., 2003; Jo, Asparouhov, et al., in press), as well as the intervention’s effect on different mediators (MacKinnon and Dwyer, 1993; MacKinnon, Weber, and Pentz, 1989; Tein, Sandler, et al., 2004; MacKinnon, Lockwood, et al., 2007; MacKinnon, 2008).


As the field of prevention science matures, important new developments in methodological research will be needed to meet new challenges. Some of these challenges include (1) integrating structural and functional imaging data on the brain; (2) understanding how genetics, particularly gene–environment interactions, can best inform prevention; (3) testing and evaluating implementation strategies for prevention programs; and (4) modeling and expressing effects of prevention for informing public policy.

Incorporating imaging and genetics data into analyses will require the ability to deal with huge numbers of voxels, polymorphisms, and expressed genes. The large literature on data reduction techniques and multiple comparisons may provide a basis for methods for studying mediational pathways, expressed genes, and gene–environment interactions that may influence prevention outcomes and should be considered in intervention designs. Also, as the body of evidence for effective programs continues to grow, demand will increase for evaluations of alternative strategies for implementing such programs. Finally, the ability to model the costs as well as the effectiveness of different preventive interventions for communities

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