Adaptive treatment strategies in chronic disease.

An adaptive treatment strategy (ATS) is a rule for adapting a treatment plan to a patient's history of previous treatments and the response to those treatments. The ongoing management of chronic disease defines an ATS, which may be implicit and hidden or explicit and well-specified. The ATS is characterized by the use of intermediate, early markers of response to dynamically alter treatment decisions, in order to achieve a favorable ultimate outcome. We illustrate the ATS concept and describe how the effect of initial treatment decisions depends on the performance of subsequent decisions at later stages. We show how to compare two or more ATSs, or to determine an optimal ATS, using a sequential multiple assignment randomized (SMAR) trial. Designers of clinical trials might find the ATS concept useful in improving the efficiency and ecological relevance of clinical trials.

[1]  J. Robins.  The control of confounding by intermediate variables. , 1989, Statistics in medicine.

[2]  R. Kleiger,et al.  Correction: Outcomes in Patients with Acute Non-Q-Wave Myocardial Infarction Randomly Assigned to an Invasive as Compared with a Conservative Management Strategy. , 1998, The New England journal of medicine.

[3]  P W Lavori,et al.  Developing and comparing treatment strategies: an annotated portfolio of designs. , 1998, Psychopharmacology bulletin.

[4]  Philip W. Lavori,et al.  A design for testing clinical strategies: biased adaptive within‐subject randomization , 2000 .

[5]  Ree Dawson,et al.  Flexible treatment strategies in chronic disease: clinical and research implications , 2000, Biological Psychiatry.

[6]  J M Robins,et al.  Marginal Mean Models for Dynamic Regimes , 2001, Journal of the American Statistical Association.

[7]  P. Lavori,et al.  A controlled trial of inpatient and outpatient geriatric evaluation and management. , 2002, The New England journal of medicine.

[8]  S. Murphy,et al.  Optimal dynamic treatment regimes , 2003 .

[9]  Ree Dawson,et al.  Dynamic treatment regimes: practical design considerations , 2004, Clinical trials.

[10]  James M. Robins,et al.  Optimal Structural Nested Models for Optimal Sequential Decisions , 2004 .

[11]  S. Murphy,et al.  An experimental design for the development of adaptive treatment strategies , 2005, Statistics in medicine.

[12]  Randy D Gascoyne,et al.  Rituximab-CHOP versus CHOP alone or with maintenance rituximab in older patients with diffuse large B-cell lymphoma. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  Erica E M Moodie,et al.  Demystifying Optimal Dynamic Treatment Regimes , 2007, Biometrics.

[14]  Ree Dawson,et al.  Improving the efficiency of estimation in randomized trials of adaptive treatment strategies , 2007, Clinical trials.

[15]  P. Lavori,et al.  Sequential causal inference: Application to randomized trials of adaptive treatment strategies , 2008, Statistics in medicine.

[16]  Peter F Thall,et al.  A review of phase 2–3 clinical trial designs , 2008, Lifetime data analysis.