The overarching goal of this renewal application is to develop and disseminate novel methods of design and analysis for effectiveness research using methodology for 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. The classic randomized controlled trial has 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. Nonetheless, the 'fixed- treatment'trial continues to be a mainstay of much of psychiatric research. The 'fixed'study protocol precludes adaptive changes to treatment for patients who do not fully respond to assigned treatment, typically leading to problematic rates of nonadherence and dropout. The uncontrolled treatment decisions that are intrinsic to nonadherence jeopardize the power and interpretability of intent-to-treat (ITT) comparisons, but reflect a lack of fit between clinical reality and the fixed-treatment design, rather than a weakness of ITT. The proposed aims extends ES-SMAR to (1) develop designs to close the gap between ITT inference and clinically relevant inference, using ATS that preempt nonadherence, (2) develop methods to address the external validity of ITT comparisons based on multi-stage trials of ATS, using single-stage effectiveness trials and well designed observational studies for calibration. 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 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.

Public Health Relevance

Nonadherence to prescribed medication and therapy pervades the treatment of most psychiatric disoders. It complicates experimental evaluation of new treatments, but because nonadhernce may lead to worse patient outcomes, it also complicates decision making by the clinician in practice settings. New approaches to clinical trial design that preempt or reduce nonadherence in the experiment and the real world are needed. This proposal will help develop the new design for experiments and the methods needed for their analysis

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
2R01MH051481-12A1
Application #
8231828
Study Section
Mental Health Services in Non-Specialty Settings (SRNS)
Program Officer
Rupp, Agnes
Project Start
1995-04-01
Project End
2014-12-31
Budget Start
2012-01-13
Budget End
2012-12-31
Support Year
12
Fiscal Year
2012
Total Cost
$310,950
Indirect Cost
$76,075
Name
Stanford University
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Lavori, Philip W; Dawson, Ree (2014) Introduction to dynamic treatment strategies and sequential multiple assignment randomization. Clin Trials 11:393-399
Geller, Barbara; Luby, Joan L; Joshi, Paramjit et al. (2012) A randomized controlled trial of risperidone, lithium, or divalproex sodium for initial treatment of bipolar I disorder, manic or mixed phase, in children and adolescents. Arch Gen Psychiatry 69:515-28
Dawson, Ree; Lavori, Philip W (2012) Efficient design and inference for multistage randomized trials of individualized treatment policies. Biostatistics 13:142-52
Fiore, Louis D; Brophy, Mary; Ferguson, Ryan E et al. (2011) A point-of-care clinical trial comparing insulin administered using a sliding scale versus a weight-based regimen. Clin Trials 8:183-95
Dawson, Ree; Lavori, Philip W (2010) Sample size calculations for evaluating treatment policies in multi-stage designs. Clin Trials 7:643-52
Lavori, Philip W; Brown, C Hendricks; Duan, Naihua et al. (2008) Missing Data in Longitudinal Clinical Trials Part A: Design and Conceptual Issues. Psychiatr Ann 38:784-792
Lavori, Philip W; Dawson, Ree (2008) Adaptive treatment strategies in chronic disease. Annu Rev Med 59:443-53
Dawson, Ree; Green, Alan I; Drake, Robert E et al. (2008) Developing and testing adaptive treatment strategies using substance-induced psychosis as an example. Psychopharmacol Bull 41:51-67
Dawson, Ree; Lavori, Philip W (2008) Sequential causal inference: application to randomized trials of adaptive treatment strategies. Stat Med 27:1626-45
Lavori, Philip W; Dawson, Ree (2007) Improving the efficiency of estimation in randomized trials of adaptive treatment strategies. Clin Trials 4:297-308

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