Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual's status and progress should be used in practice to decide which treatment (e.g., type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Further, in many health domains, count data are overdispersed - having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation.
翻译:在许多卫生领域,被称为治疗算法或适应性干预的动态治疗方案(DTRs)在许多动态治疗办法(DTRs)中发挥着日益重要的作用。DTRs具有满足个人独特和不断变化的需要的动机,必要时提供所需的治疗类型,同时尽量减少不必要的治疗。实际上,DTR是决策规则的序列,它规定,对于具有纵向计数结果的SMARTs, 抽样规模估计方法存在重大差距。此外,在许多卫生领域,计数数据过于分散 -- -- 其差异大于平均值。我们建议采用基于Monte Carlo的抽样规模估计方法,适用于许多类型的长度结果,并提供长度治疗病人长期超常治疗的案例研究。</s>