Chronic mental illnesses such as major depression and schizophrenia impose a heavy burden both on the individual and society. High quality clinical care can reduce the burden but such care demands sequential clinical decision making concerning treatment. The long term goal of this project is to improve sequential clinical decision making.
The specific aims concern methodological approaches for helping clinicians address questions such as: How long should one wait to decide that a patient is not deriving sufficient benefit from treatment? If it is established that a patient is not sufficiently benefiting from treatment then which treatment should be provided next? Should the sequence of treatments vary according to patient characteristics and outcomes such as disease features, response, side effect burden, and adherence? In this project methods taken from computer science, engineering and statistics are generalized and adapted for use with clinical trial data so as to address these kinds of questions. The methods will be developed and validated using data from two large NIMH funded trials. This project will be conducted by a collaborative team involving a computer scientist, two psychiatrists and a statistician.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH080015-04
Application #
7849489
Study Section
Mental Health Services in MH Specialty Settings (SRSP)
Program Officer
Rupp, Agnes
Project Start
2007-05-01
Project End
2012-04-30
Budget Start
2010-05-01
Budget End
2011-04-30
Support Year
4
Fiscal Year
2010
Total Cost
$466,219
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Nahum-Shani, Inbal; Ertefaie, Ashkan; Lu, Xi Lucy et al. (2017) A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders. Addiction 112:901-909
Almirall, Daniel; Griffin, Beth Ann; McCaffrey, Daniel F et al. (2014) Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals. Stat Med 33:3466-87
Shortreed, Susan M; Laber, Eric; Scott Stroup, T et al. (2014) A multiple imputation strategy for sequential multiple assignment randomized trials. Stat Med 33:4202-14
Laber, Eric B; Lizotte, Daniel J; Qian, Min et al. (2014) Dynamic treatment regimes: technical challenges and applications. Electron J Stat 8:1225-1272
Fonteneau, Raphael; Murphy, Susan A; Wehenkel, Louis et al. (2013) Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories. Ann Oper Res 208:383-416
Almirall, Daniel; McCaffrey, Daniel F; Ramchand, Rajeev et al. (2013) Subgroups analysis when treatment and moderators are time-varying. Prev Sci 14:169-78
Little, Roderick J; D'Agostino, Ralph; Cohen, Michael L et al. (2012) The prevention and treatment of missing data in clinical trials. N Engl J Med 367:1355-60
Almirall, Daniel; Lizotte, Daniel J; Murphy, Susan A (2012) SMART Design Issues and the Consideration of Opposing Outcomes: Discussion of ""Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer"" by by Wang, Rotnitzky, Lin, Millikan, and Thall. J Am Stat Assoc 107:509-512
Almirall, Daniel; Compton, Scott N; Rynn, Moira A et al. (2012) SMARTer discontinuation trial designs for developing an adaptive treatment strategy. J Child Adolesc Psychopharmacol 22:364-74
Nahum-Shani, Inbal; Qian, Min; Almirall, Daniel et al. (2012) Experimental design and primary data analysis methods for comparing adaptive interventions. Psychol Methods 17:457-477

Showing the most recent 10 out of 39 publications