Transcript Assessing the Total Effect of Time

Methodology for
Adaptive Treatment Strategies
R21 DA019800
S.A. Murphy
For MCATS
Oct. 8, 2009
Overview
• Network involving computer scientists,
engineers, physicians (mental health, infectious
disease, substance abuse ), psychologists and
statisticians.
• Goal: Identify major challenges & kick-start
collaborations leading to longer term research
initiatives
• Two workshops; white paper; special issue of
Drug and Alcohol Dependence in 2007
• September 2004 - August 2006
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Some Consequences
• A number of funded grants: R01s and a P01
• Many papers + book by J. McKay : Treating
Substance Use Disorders With Adaptive
Continuing Care.
• Summer program (2007) for computer
scientists, engineers and statisticians at the
Statistical and Applied Mathematical Sciences
Institute.
• Clinical trials designed to inform adaptive
treatment strategies
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Adaptive Treatment Strategies operationalize multistage decision making.
These are individually tailored sequences of treatments,
with treatment type and dosage adapted to the
individual.
•Generalization from a one-time decision to a
sequence of decisions concerning treatments
•Operationalize clinical practice.
Each decision corresponds to a stage of treatment
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Adaptive Drug Court Program
non-responsive
low risk
As-needed court hearings
+ standard counseling
As-needed court hearings
+ ICM
non-compliant
non-compliant
high risk
non-responsive
Bi-weekly court hearings
+ standard counseling
Bi-weekly court hearings
+ ICM
non-compliant
Court-determined
disposition
Critical Questions
•What is the best sequencing of treatments? Which
treatment to provide first, second?
•What is the best timings of alterations in treatments?
What information do we use to make these decisions?
(how do we individualize the sequence of treatments?)
Methodological Innovations
• New experimental designs for comparing
and constructing adaptive treatment
strategies: SMART
• Transfer/generalization of data analysis
methods for multi-stage decision making
from the fields of computer science and
engineering: Q-Learning
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SMART
Sequential Multiple Assignment Randomized Trial
These are multi-stage trials; individuals move through
multiple stages of treatment and are initially randomized
and then re-randomized at each stage. Each stage
corresponds to a critical decision.
SMART
• Precursors of the SMART design:
•CATIE (2001), STAR*D (2003), many in cancer
•SMART designs:
•Treatment of Alcohol Dependence (Oslin, data
analysis; NIAAA)
•Treatment of ADHD (Pelham, data analysis ; IES)
Treatment of Drug Abusing Pregnant Women (Jones, in
field; NIDA)
•Treatment of Autism (Kasari, in field; Foundation)
•Treatment of Alcoholism (McKay, in field; NIAAA)
•Treatment of Prostate Cancer (Millikan, 2007)
Alcohol Dependence
(Oslin; NIAAA)
Naltrexone
8 wks Response
Random
assignment:
Early Trigger for
Nonresponse
Random
assignment:
TDM + Naltrexone
CBI
Nonresponse
CBI +Naltrexone
Random
assignment:
8 wks Response
Naltrexone
Random
assignment:
TDM + Naltrexone
Late Trigger for
Nonresponse
Random
assignment:
Nonresponse
CBI
CBI +Naltrexone
Does improving adherence help?
Naltrexone
8 wks Response
Random
assignment:
Early Trigger for
Nonresponse
Random
assignment:
TDM + Naltrexone
CBI
Nonresponse
CBI +Naltrexone
Random
assignment:
8 wks Response
Naltrexone
Random
assignment:
TDM + Naltrexone
Late Trigger for
Nonresponse
Random
assignment:
Nonresponse
CBI
CBI +Naltrexone
Least Intensive vs Most Intensive
Naltrexone
8 wks Response
Random
assignment:
Early Trigger for
Nonresponse
Random
assignment:
TDM + Naltrexone
CBI
Nonresponse
CBI +Naltrexone
Random
assignment:
8 wks Response
Naltrexone
Random
assignment:
TDM + Naltrexone
Late Trigger for
Nonresponse
Random
assignment:
Nonresponse
CBI
CBI +Naltrexone
Drug-Addicted Pregnant Women
(Jones; NIDA)
rRBT
2 wks Response
Random
assignment:
tRBT
Random
assignment:
tRBT
tRBT
Nonresponse
eRBT
Random
assignment:
2 wks Response
aRBT
Random
assignment:
rRBT
rRBT
Random
assignment:
Nonresponse
tRBT
rRBT
ADHD (Pelham, IES)
A1. Continue, reassess monthly;
randomize if deteriorate
Yes
8 weeks
A. Begin low-intensity
behavior modification
A2. Add medication;
BEMOD remains stable but
medication dose may vary
AssessAdequate response?
No
Random
assignment:
Random
assignment:
A3. Increase intensity of BEMOD
with adaptive modifications based on impairment
B1. Continue, reassess monthly;
randomize if deteriorate
8 weeks
B. Begin low dose
medication
AssessAdequate response?
No
Random
assignment:
B2. Increase dose of medication
with monthly changes
as needed
B3. Add BEMOD
treatment with adaptive
Modifications based on impairment;
medication dose
remains stable
Q-Learning is used to constructing
proposals for more deeply tailored
adaptive treatment strategies
Q stands for “Quality of Treatment”
Q-Learning is a generalization of regression to
multistage treatment
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Example of Q-Learning output (CATIE)
Begin with Olanzapine
If non-responder then
If preference is to try for efficacy improvement then
If PANSS > 94 then switch to Clozapine
Else switch to either Quetiapine or Risperidone
If preference is to try for tolerable med. then
If Olanzapine was not tolerable then switch to
Risperidone
If Olanzapine was not efficacious then switch to
Quetiapine
PANSS: Positive and Negative Syndrome Scale
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Acknowledgements: This presentation is based on
work with MCAT members as well as many
individuals including Linda Collins, Dave Oslin,
Joelle Pineau, John Rush and Scott Stroup.
Email address: [email protected]
Slides with notes at:
http://www.stat.lsa.umich.edu/~samurphy/
Click on seminars > health science seminars
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Why use an Adaptive Treatment
Strategy?
– High heterogeneity in response to any one
intervention
• What works for one person may not work for
another
• What works now for a person may not work later
– Improvement often marred by relapse
• Remitted or few current symptoms is not the same
as cured.
– Co-occurring disorders/adherence problems are
common
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