Prevention scientists, intervention developers, patients, providers, and clients are continually seeking more effective and efficient treatments for a wide range of social, behavioral, and public health problems. Across this range of problems, there is a common interest in developing a better understanding of impacts of interventions on specific subgroups. Among policymakers, an interest in the question “What works?” is now often accompanied by “What works for whom?” For example, the Obama administration has been emphasizing the use of rigorous research as part of evidence-based policy-making. In a 2009 memorandum to federal agencies and departments, the Office ofManagement and Budget emphasized both the importance of rigorous research on program effectiveness as well as evidence aimed at improving the life outcomes of individuals. In medicine and health policy, there has been a strong push toward comparative effectiveness research; the purpose of which is “to inform patients, providers, and decision-makers, responding to their expressed needs about which interventions are most effective for which patients under specific circumstances” (Federal Coordinating Council for Comparative Effectiveness Research 2009, p 3). One implication of this emphasis on what works has been greater attention to the research and methodologies associated with specific populations and subgroups. The analysis of subgroups can matter a great deal in prevention science and intervention research. First, many prevention scientists use subgroup findings to unpack significant main effects or to investigate why there was a lack of significant main effects. Prevention scientists frequently want to explore whether a program was more or less effective for a segment of the target population and why it may have been less effective for another subgroup. Rothwell (2005) argues that the importance of subgroup analysis is not in differential response to treatment, but in identifying how to maximize benefits from treatments and mitigate risk. Rothman (2012) highlights that moderated effects can help prevention scientists to refine theory, tailor prevention to specific contexts or to the needs of specific populations, or target intervention. Policy-relevant research around prevention and intervention science is regularly challenged to answer the question of what works for whom. Subgroup analysis can be seen to be directly linked to policy decisions around programmatic aims (e.g., Upward Bound), funding decisions (e.g., Even Start), and new initiatives targeting funding towards evidence-based programs (e.g., teen pregnancy and home visitation). Subgroup analysis, broadly, aims to measure change within and between groups. Subgroups are defined by characteristics measured at baseline. Subgroups can be characterized by variables that are easier to define, such as age, to those less well-defined, such as risk status. Subgroups can be continuous or categorical; variables with low, moderate, or high measurement error; and variables that are measured, latent, or estimated based on response to treatment. Subgroups can include individual characteristics or site-level variables. Subgroups may occur with more regularity in a population, such as gender, more infrequently such as families with multiple risk factors, or more difficult to represent in large numbers in prevention trials such as rural communities. Work by Rothwell (2005) and Wang et al. (2007) highlighted the issues with subgroup analysis within medical research. Rothwell (2005) specified best practices in study design, L. H. Supplee (*) : B. C. Kelly Office of Planning, Research and Evaluation, Administration for Children and Families, 370 L’Enfant Promenade SW, 7th Fl West, Washington, DC 20447, USA e-mail: lauren.supplee@acf.hhs.gov
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