摘要：Dynamic treatment regimes are an emerging and important methodological area in health research, particularly in the management of chronic health conditions. This paradigm encompasses the ideological shift in research from the acute care model to the chronic care model. It allows individualization of treatment (type, dosage, timing) at each stage of intervention. Constructing evidence-based dynamic treatment regimes requires implementation of cutting-edge design and analysis tools. Here I briefly discuss some of these modern tools, namely the sequential multiple assignment randomized trial (SMART) design and a regression-based analysis approach called Q-learning. Chronic disorders are one of today's most pressing public health issues in both the American 1 and global 2 arenas. For example, widely prevailing conditions such as hypertension, obesity, diabetes, nicotine addiction, alcohol and drug abuse, HIV infection, and depression are all chronic. In many cases, effective long-term care of patients with these chronic conditions requires ongoing medical intervention following the chronic care model, 1 , 3 rather than the more traditional acute care model. Some of the key features of health care emphasized by the chronic care model are individualization of care according to patient needs, optimization of patient outcomes through a series of interventions, and health services based on evidence (as opposed to expert opinion only). First, in this paradigm clinicians treat patients in multiple stages, individualizing treatment type or dosage according to ongoing measures of patient response, adherence, burden, side effects, and preference. Second, instead of determining a single course of treatment (static treatment), clinicians sequentially make decisions about what to do next to optimize patient outcomes given their case history (dynamic treatment). The primary motivations for considering sequences of treatments are high interpatient variability in response to treatment, probability of relapse, presence or emergence of comorbid conditions, time-varying severity of side effects, and reduction of costs and burden when intensive treatment is unnecessary. 4 Third, although there exist traditional practice guidelines for clinicians that are primarily based on expert opinions, the chronic care model advocates that these regimes be more objective and evidence based. In fact, Wagner et al. described the chronic care model as “a synthesis of evidence-based system changes intended as a guide to quality improvement and disease management activities.”3(p69) In this context, dynamic treatment regimes (DTRs) offer a way to operationalize the sequential decision-making process involved in adaptive clinical practice and thereby a potential way to improve it. Formally, a DTR is a sequence of decision rules, 1 per stage of intervention. Each decision rule takes a patient's individual characteristics and treatment history observed up to a given stage as input and offers a recommended treatment at that stage (recommendations can include treatment type, dosage, and timing). Conceptually, a DTR can be viewed as a decision support system, which is 1 of the 6 elements of the chronic care model. 3 DTRs are developed to define the sequence of treatments that will result in the most favorable clinical outcome possible. A DTR is optimal if it optimizes the mean long-term outcome (e.g., the outcome observed at the end of the final stage of treatment). A concrete example of a DTR, originally described by Murphy, 5 can serve as an illustration.
