Murphy Susan A, Oslin David W, Rush A John, Zhu Ji
Institute for Social Research, University of Michigan, Ann Arbor, MI 48106-1248, USA.
Neuropsychopharmacology. 2007 Feb;32(2):257-62. doi: 10.1038/sj.npp.1301241. Epub 2006 Nov 8.
Psychiatric disorders are often chronic conditions that require sequential decision making to achieve the best clinical outcomes. Sequential decisions are necessary to accommodate treatment response heterogeneity, a variable course of illness, and the often heavy burden associated with intensive or longer-term treatment. Yet, only a few studies in this field have been designed to address sequential decisions. Most of the experimental designs and data analytic methods that are best suited for improving sequential clinical decision making are often found in nonmedical fields such as engineering, computer science, and statistics. Promising designs and methods are surveyed with a focus on those areas most immediately useful for informing clinical decision making.
精神疾病通常是慢性病，需要进行序贯决策以实现最佳临床结果。序贯决策对于适应治疗反应的异质性、疾病的可变进程以及强化或长期治疗通常带来的沉重负担是必要的。然而，该领域仅有少数研究旨在解决序贯决策问题。最适合改善序贯临床决策的大多数实验设计和数据分析方法通常见于工程、计算机科学和统计学等非医学领域。本文对有前景的设计和方法进行了综述，重点关注那些对指导临床决策最直接有用的领域。
