2DkcTXceO

364 Statistics and public health researchreason for this discrepancy is that prevention may not share the same fiscalincentives as treatment, since persons whose disease has been delayed oraverted are usually not individually identifiable. Furthermore, the types ofspecimens needed for relevant biological measures (e.g., gene expression profiles),are frequently not available in the context of studies of large cohorts ofhealthy persons. As a result, preventive interventions that have been studiedin randomized controlled trials have mostly involved pill taking approaches,with rationale derived from observational epidemiology or borrowed from precedingtherapeutic trials.Specifically, there have been few trials of behavioral interventions withchronic disease outcomes. As an exception, the Diabetes Prevention ProgramResearch Group (Diabetes Prevention Program Research Group, 2002) randomizedtrial among 3234 persons having impaired glucose tolerance demonstrateda major benefit for Type 2 diabetes incidence with a combined dietaryand physical activity intervention. Also, the Women’s Health Initiative lowfatdietary modification trial (Prentice et al., 2006) among 48,835 ostensiblyhealthy postmenopausal women demonstrated a modest reduction in its breastcancer primary outcome, but the reduction didn’t meet the usual requirementsfor statistical significance (log-rank significance level of .07). There has neverbeen a full-scale physical activity intervention trial with chronic disease outcomes.Statistical methods for high-dimensional data analysis and biological networkdevelopment may be able to help fill the preventive intervention developmentgap. For example, changes in proteomic or metabolomic profiles maybe able to combine with changes in conventional risk factors for targeted diseasesin intermediate outcome intervention trials of practical size and expenseto select among, and provide the initial evaluation of, potential preventiveinterventions in a manner that considers both efficacy and safety.Also, because of cost and logistics, few full-scale disease prevention trialscan be conducted, regardless of the nature of the intervention. Innovativehybrid designs that combine the rather comprehensive profiling of the previousparagraph with case-control data for targeted outcomes that also include thesame types of high-dimensional biologic data may be able to produce testsof intervention effects on chronic disease of acceptable reliability for mostpurposes, at costs that are not extreme. Interventions meeting criteria in suchhybrid designs, that also have large public health potential, could then be putforward with a strong rationale for the few full-scale randomized trials withdisease outcomes that can be afforded.