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Piloting and Sizing Sequential Multiple Assignment Randomized Trials in Dynamic Treatment Regime Development PowerPoint Presentation
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Piloting and Sizing Sequential Multiple Assignment Randomized Trials in Dynamic Treatment Regime Development

Piloting and Sizing Sequential Multiple Assignment Randomized Trials in Dynamic Treatment Regime Development

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Piloting and Sizing Sequential Multiple Assignment Randomized Trials in Dynamic Treatment Regime Development

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  1. Piloting and Sizing Sequential Multiple AssignmentRandomized Trials in Dynamic Treatment Regime Development 2012 Atlantic Causal Inference Conference May 25, 2012—Johns Hopkins Daniel Almirall & Susan A. Murphy TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA

  2. Outline Dynamic Treatment Regimes Sequential Multiple Assignment Randomized Trial (SMART) External Pilots Tailoring Variables Transition to Next Stage Assessment Schedule Sizing a Pilot SMART

  3. Dynamic treatment regimes are individually tailored sequences of treatments, with treatment type and dosage changing according to patient outcomes. Operationalizesclinical practice. k Stages for one individual Patient data available at jth stage Action at jth stage (usually a treatment)

  4. Dynamic Treatment Regimes • Adynamic treatment regime (DTR) is a sequence of decision rules, one per treatment stage. • Each decision rule inputs one or more tailoring variables and outputs a treatment action. • The tailoring variables are (summaries of) patient data available at each stage.

  5. Example of a Dynamic Treatment Regime (DTR) • Adaptive Drug Court Program for drug abusing offenders. • Goal is to minimize recidivism and drug use. • Marlowe et al. (2008, 2009)

  6. Adaptive Drug Court Program

  7. Sequential, Multiple Assignment, Randomized Trial (SMART) At each stage subjects are randomized among alternative options. For k=2, data on each subject is of form: Aj is a randomized treatment action with known randomization probability.

  8. Usually the treatment options for A2 are restricted by the values of one or more summaries of (X1, A1, X2) • These summaries are embeddedtailoring variables; they are embedded in the experimental design. • The embedded tailoring variable(s) restrict the class of DTRs that can be investigated using data from the SMART.

  9. Pelham ADHD Study Continue, reassess monthly; randomize if deteriorate Yes 8 weeks Begin low-intensity BMOD BMOD + Med Assess- Adequate response? Randomassignment: No BMOD++ Randomassignment: Continue, reassess monthly; randomize if deteriorate Yes 8 weeks Med ++ Begin low dose Med Assess- Adequate response? Randomassignment: BMOD + Med No

  10. ADHD: Embedded Tailoring Variable • Early response is determined by two teacher- rated instruments, ITB and IRS. • Binary embedded tailoring variable • R=0 if ITB<.75 and one or more subscales of IRS >3; otherwise R=1. • R is the embedded tailoring variable.

  11. External Pilot Studies Goal is to examine feasibility of full-scale trial. Can investigator execute the trial design? Will participants tolerate treatment? Do co-investigators buy-in to study protocol? To manualize treatment(s) To devise trial protocol quality control measures Goal is notto obtain preliminary evidence about efficacy of treatment/strategy. Rather, in the design of the full-scale SMART, the min. detectable effect size comes from the science.

  12. Embedded Tailoring Variable Don’t use an embedded tailoring variable unless the science demands it. If you have an embedded tailoring variable make it simple (e.g. binary measure of (non-) response) Non-responders likely to fail if continue on current treatment OR responders unlikely to gain much benefit if they stay on current treatment. Usually need to use analyses of existing data to justify the use of the tailoring variable

  13. Jones’ Study for Drug-Addicted Pregnant Women Decrease scope/intensity 2 wks Response Randomassignment: Continue on same tRBT Continue on same Randomassignment: Nonresponse Increase scope/intensity Randomassignment: Decrease scope/intensity 2 wks Response Randomassignment: Continue on same rRBT Randomassignment: Continue on same Nonresponse Increase scope/intensity

  14. Missing Tailoring Variable How to manage missingness in the embedded tailoring variable for purposes of randomizing/assigning subsequent treatment? VERY different from handling missing data in a statistical analysis. Tailoring variable is part of the definition of the treatment and experimental design.

  15. Missing Tailoring Variable Need to formulate a fixed, pre-specified rule to determine subsequent treatment if tailoring variable is missing. Unexcused visit==non-response Use a rule that depends on all observed data, including the data collected when the subjectagain shows up at a clinic visit. Try out the rule in pilot.

  16. Assessment Schedule How often should the tailoring variable be measured? Example: Alcoholism study with weekly assessments of days of heavy drinking. Weekly assessments were insufficient and likely a pilot study would have detected this.

  17. Oslin’s ExTENd Study Naltrexone 8 wks Response Randomassignment: TDM + Naltrexone Nonresponse if HDD >1 CBI Randomassignment: Nonresponse CBI +Naltrexone Randomassignment: Naltrexone 8 wks Response Randomassignment: TDM + Naltrexone Nonresponse if HDD>4 Randomassignment: CBI Nonresponse CBI +Naltrexone

  18. Outcome Assessment versus Tailoring Variable Assessment Keep these separate. Tailoring variable assessment done at clinic visit by clinical staff or clinical lab or participant. Outcome assessment done at research visit by independent evaluator or independent lab or participant. Autism & Adolescent Depression Examples Try out in Pilot Study

  19. Transition Between Stages Clinical staff disagree with when 2nd stage treatment is introduced. Non-responding subject refuses 2nd stage treatment. This may be VERY important scientifically Cocaine/Alcoholism Example Test in Pilot

  20. Sample Size for a SMART Pilot Primary feasibility aim is to ensure investigative team has opportunity to implement protocol from start to finish with sufficient numbers If investigator has good evidence to guess the response rate: Choose pilot sample size so that with probability q, at least m participants fall into the sub-groups (the “small cells”) If little to no evidence concerning response rate, size the study to estimate the response rate with a given confidence interval width.

  21. Pelham ADHD Study Continue, reassess monthly; randomize if deteriorate Yes 8 weeks Begin low-intensity BMOD BMOD + Med Assess- Adequate response? Randomassignment: No BMOD++ Randomassignment: Continue, reassess monthly; randomize if deteriorate Yes 8 weeks Med ++ Begin low dose Med Assess- Adequate response? Randomassignment: BMOD + Med No

  22. Sample Size for a SMART Pilot There are 2 treatment actions in stage 1, kR treatments for responders, kNR treatments for non-responders. Investigator chooses q (say 80%) and m (say 3), and assumes overall non-response rate pNR(say 50%). Solve for N, the total sample size, where

  23. Discussion SMART clinical trial designs are of growing interest in the clinical sciences. Because these designs are very new, they require a great deal of leadership on the part of the statistical community. The payoff for the statistician is Inform clinical science in a novel manner Unusual and novel trial data for methodological development

  24. This seminar can be found at: http://www.stat.lsa.umich.edu/~samurphy/ seminars/ACIC_2012.ppt Reference: Almirall D, Compton SN, Gunlicks-Stoessel M, Duan N, Murphy SA. Designing a Pilot SMART for Developing an Adaptive Treatment Strategy. To appear in Statistics in Medicine 24