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Summary.A dynamic treatment regime is a list of decision rules, one per time interval, for how the level of treatment will be tailored through time to an individual's changing status. The goal of this paper is to use experimental or observational data to estimate decision regimes that result in a maximal mean response. To explicate our objective and to state the assumptions, we use the potential outcomes model. The method proposed makes smooth parametric assumptions only on quantities that are directly relevant to the goal of estimating the optimal rules. We illustrate the methodology proposed via a small simulation.

Abstract Background The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment. Purpose Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusions As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention

Because many illnesses show heterogeneous response to treatment, there is increasing interest in individualizing treatment to patients [11]. An individualized treatment rule is a decision rule that recommends treatment according to patient characteristics. We consider the use of clinical trial data in the construction of an individualized treatment rule leading to highest mean response. This is a difficult computational problem because the objective function is the expectation of a weighted indicator function that is non-concave in the parameters. Furthermore there are frequently many pretreatment variables that may or may not be useful in constructing an optimal individualized treatment rule yet cost and interpretability considerations imply that only a few variables should be used by the individualized treatment rule. To address these challenges we consider estimation based on l1 penalized least squares. This approach is justified via a finite sample upper bound on the difference between the mean response due to the estimated individualized treatment rule and the mean response due to the optimal individualized treatment rule.

In this article two new methods for building and evaluating e-health interventions are described. The first is the Multiphase Optimization Strategy (MOST). MOST consists of a screening phase, in which intervention components are efficiently identified for selection for inclusion in an intervention or rejection, based on their performance; a refining phase, in which the selected components are finetuned, and questions such as optimal component dosage are investigated; and a confirming phase, in which the optimized intervention, consisting of optimal doses of the selected components, is evaluated in a standard randomized confirmatory trial. The second is the Sequential Multiple Assignment Randomized Trial (SMART) which is an innovative research design especially suited for building time-varying adaptive interventions. A SMART trial can be used to identify the best tailoring variables and decision rules for an adaptive intervention empirically. Both the MOST and SMART approaches use randomized experimentation to enable valid inferences. When properly implemented, these approaches will lead to the development of more potent e-health interventions.There are good reasons to believe that interventions based on e-health principles have the potential for considerable public health impact. Perhaps the most obvious reason is the reach of these interventions. Once an electronic intervention has been designed and programmed, delivery occurs via methods such as the Internet or by mailing a CD, and therefore is extremely convenient. Moreover, the incremental cost of delivering an intervention to additional people is usually negligible, certainly in comparison to traditional interventions where in order to reach more recipients it becomes necessary to add additional physicians, therapists, health educators, peer counselors, and so on to deliver the program. The limiting factor for reach of an eintervention is less likely to be a shortage of resources for delivering the program electronically than access to computers on the part of potential recipients. However, access to computers continues to increase in all strata of American society, suggesting that e-health interventions hold growing promise. 1Correspondence and reprint requests: Linda M. Collins

In adaptive treatment strategies, the treatment level and type is repeatedly adjusted according to ongoing individual response. Since past treatment may have delayed effects, the development of these treatment strategies is challenging. This paper advocates the use of sequential multiple assignment randomized trials in the development of adaptive treatment strategies. Both a simple ad hoc method for ascertaining sample sizes and simple analysis methods are provided.

Recently, adaptive interventions have emerged as a new perspective on prevention and treatment.Adaptive interventions resemble clinical practice in that different dosages of certain prevention or treatment components are assigned to different individuals, and/or within individuals across time, with dosage varying in response to the intervention needs of individuals. To determine intervention need and thus assign dosage, adaptive interventions use prespecified decision rules based on each participant's values on key characteristics, called tailoring variables. In this paper, we offer a conceptual framework for adaptive interventions, discuss principles underlying the design and evaluation of such interventions, and review some areas where additional research is needed. Keywords adaptive interventions; prevention; research designFor most of the history of research-based interventions aimed at prevention and treatment, the composition and dosage of these interventions have been fixed, in other words, a single composition and dosage has been offered to all program participants. For example, a schoolbased drug abuse prevention curriculum might be delivered to all sixth graders. Every component of the intervention that may be necessary for any particular participant is included in the curriculum, and each child is given the same intervention. Although it is recognized that individuals may have different intervention needs, it is expected that the intervention is in no way diluted or made counterproductive if components that are particularly relevant for an individual are combined with components that may have less, or even no, relevance for that individual.Recently, adaptive interventions have emerged as a new perspective on research-based prevention and treatment. According to this perspective, the varying intervention needs of individuals may not be met optimally by using a single uniform composition and dosage. For this reason, an adaptive intervention assigns different dosages of certain program components across individuals, and/or within individuals across time. Dosage varies in response to the intervention needs of individuals, and dosages are assigned based on decision rules linking characteristics of the individual with specific levels and types of

Interventions often involve a sequence of decisions. For example, clinicians frequently adapt the intervention to an individual’s outcomes. Altering the intensity and type of intervention over time is crucial for many reasons, such as to obtain improvement if the individual is not responding or to reduce costs and burden when intensive treatment is no longer necessary. Adaptive interventions utilize individual variables (severity, preferences) to adapt the intervention and then dynamically utilize individual outcomes (response to treatment, adherence) to readapt the intervention. The Sequential Multiple Assignment Randomized Trial (SMART)provides high-quality data that can be used to construct adaptive interventions. We review the SMART and highlight its advantages in constructing and revising adaptive interventions as compared to alternative experimental designs. Selected examples of SMART studies are described and compared. A data analysis method is provided and illustrated using data from the Extending Treatment Effectiveness of Naltrexone SMART study.