1. ¹Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
2. ²Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
3. ³Department of Clinical Sciences, University of Texas, Southwestern Medical Center, Dallas, TX, USA
4. ⁴Department of Statistics, University of Michigan, Ann Arbor, MI, USA

Correspondence: Professor SA Murphy, Institute for Social Research, 2068, University of Michigan, 426 Thompson Street, Ann Arbor, MI 48106-1248, USA. Tel: +1 734 763 5046; Fax: +1 734 763 4676; E-mail: samurphy@umich.edu

⁵Members of the MCATS network (alphabetical order) are Satinder Baveja, PhD (University of Michigan), Linda Collins, PhD (Pennsylvania State University), Marie Davidian, PhD (North Carolina State University), Kevin Lynch, PhD (University of Pennsylvania), James McKay, PhD (University of Pennsylvania), Joelle Pineau, PhD (McGill University), Daniel Rivera, PhD (Arizona State University), Eric Rosenberg, MD (Harvard Medical School), Thomas TenHave, PhD (University of Pennsylvania), and Anastasios Tsiatis, PhD (North Carolina State University).

Received 16 February 2006; Revised 19 May 2006; Accepted 6 June 2006; Published online 8 November 2006.

Abstract

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.