The overarching goal of this renewal application is to develop and disseminate novel methods of design and analysis for clinical trials of adaptive treatment strategies (ATSs). An ATS is a dynamic algorithm for matching clinical treatment decisions to the evolving course of the individual patient's response to treatment over time, based on a list of rules that together specify the sequential, multi-stage decision making of the clinician treating a chronic disorder such as depression. Clinicians managing chronic diseases that do not admit a definitive cure, require evidence from randomized trials to support adaptive decision-making. The classic randomized controlled trial has recently been augmented to include patient and clinician preferences (Equipoise Stratification, or ES), and extended to the Sequential Multiple Assignment Randomization (SMAR) trial, a design for comparing ATSs. Prototype designs to discover the optimal symptom-based threshold for deciding when to change treatment and designs for premarketing studies of new treatments when it is desirable to avoid a placebo group have been developed. This proposal has three specific aims (1) integrate the ES and ATS concepts, explicitly considering the changes in ES over time, (2) provide methods for design and statistical analysis of studies of ATSs that include all components of clinical decision-making, such as treatment timing decisions as well as choice of next treatment, and (3) develop a method to select near-optimal ATS, to compensate for the complexity of SMAR studies and increase their feasibility. Both mathematical analysis and simulation will be used to develop and evaluate the new methods, building on recent progress in the area. The methods will also be tested on data from a recent large-scale effectiveness trial (Sequenced Treatment Alternatives to Relieve Depression - STAR*D) sponsored by the National Institute of Mental Health. The proposed new methods will be developed in the specific clinical context of mental disorders, but they are clearly relevant to many other areas of medicine. Relevance: The traditional randomized clinical trial provides the best guidance for clinicians facing a clinical decision, such as which treatment to prescribe. However, when a clinician treats a patient with a chronic disease, such as depression, there is a need for a long-term plan that adapts to the way the patient responds to the sequence of treatments being provided. Testing and comparing these adaptive treatment strategies requires new methods for doing randomized clinical trials, and this proposal will help develop those methods. ? ? ?
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