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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.
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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
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
Adaptive Treatment Strategies operationalize multi-stage 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
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 7
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 Randomassignment: TDM + Naltrexone Early Trigger for Nonresponse CBI Randomassignment: Nonresponse CBI +Naltrexone Randomassignment: Naltrexone 8 wks Response Randomassignment: TDM + Naltrexone Late Trigger for Nonresponse Randomassignment: CBI Nonresponse CBI +Naltrexone
Does improving adherence help? Naltrexone 8 wks Response Randomassignment: TDM + Naltrexone Early Trigger for Nonresponse CBI Randomassignment: Nonresponse CBI +Naltrexone Randomassignment: Naltrexone 8 wks Response Randomassignment: TDM + Naltrexone Late Trigger for Nonresponse Randomassignment: CBI Nonresponse CBI +Naltrexone
Least Intensive vsMost Intensive Naltrexone 8 wks Response Randomassignment: TDM + Naltrexone Early Trigger for Nonresponse CBI Randomassignment: Nonresponse CBI +Naltrexone Randomassignment: Naltrexone 8 wks Response Randomassignment: TDM + Naltrexone Late Trigger for Nonresponse Randomassignment: CBI Nonresponse CBI +Naltrexone
Drug-Addicted Pregnant Women (Jones; NIDA) rRBT 2 wks Response Randomassignment: tRBT tRBT tRBT Randomassignment: Nonresponse eRBT Randomassignment: aRBT 2 wks Response Randomassignment: rRBT rRBT Randomassignment: tRBT Nonresponse 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 butmedication dose may vary Assess- Adequate response? Randomassignment: No A3. Increase intensity of BEMOD with adaptive modifi-cations based on impairment Randomassignment: B1. Continue, reassess monthly; randomize if deteriorate 8 weeks B2. Increase dose of medication with monthly changes as needed B. Begin low dose medication Assess- Adequate response? Randomassignment: B3. Add BEMODtreatment with adaptive Modifications based on impairment; medication dose remains stable No
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
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 • IfOlanzapine was not tolerable then switch to Risperidone • IfOlanzapine was not efficacious then switch to Quetiapine • PANSS: Positive and Negative Syndrome Scale
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: samurphy@umich.edu Slides with notes at: http://www.stat.lsa.umich.edu/~samurphy/ Click on seminars > health science seminars
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 18