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.