Kidwell Kelley M, Hyde Luke W
Department of Biostatistics, University of Michigan.
Department of Psychology, Center for Human Growth and Development, Institute for Social Research, University of Michigan.
Am J Eval. 2016 Sep;37(3):344-363. doi: 10.1177/1098214015617013. Epub 2015 Dec 11.
Heterogeneity between and within people necessitates the need for sequential personalized interventions to optimize individual outcomes. Personalized or adaptive interventions (AIs) are relevant for diseases and maladaptive behavioral trajectories when one intervention is not curative and success of a subsequent intervention may depend on individual characteristics or response. AIs may be applied to medical settings and to investigate best prevention, education, and community-based practices. AIs can begin with low-cost or low-burden interventions and followed with intensified or alternative interventions for those who need it most. AIs that guide practice over the course of a disease, program, or school year can be investigated through sequential multiple assignment randomized trials (SMARTs). To promote the use of SMARTs, we provide a hypothetical SMART in a Head Start program to address child behavior problems. We describe the advantages and limitations of SMARTs, particularly as they may be applied to the field of evaluation.
人与人之间以及个体内部的异质性使得需要采取一系列个性化干预措施来优化个体的治疗效果。当一种干预措施无法治愈疾病且后续干预的成功可能取决于个体特征或反应时，个性化或适应性干预（AI）适用于疾病和适应不良的行为轨迹。AI可应用于医疗环境，并用于研究最佳的预防、教育和基于社区的实践。AI可以从低成本或低负担的干预措施开始，然后为最需要的人提供强化或替代干预措施。可以通过序贯多重分配随机试验（SMART）来研究在疾病、项目或学年过程中指导实践的AI。为了促进SMART的使用，我们在一个Head Start项目中提供了一个假设的SMART，以解决儿童行为问题。我们描述了SMART的优点和局限性，特别是它们在评估领域的应用。
