James R. McKay, Ph.D. University of Pennsylvania Philadelphia VAMC Sequential multiple assignment randomized trial (SMART) adaptive studies for SUD James R. McKay, Ph.D. University of Pennsylvania Philadelphia VAMC

Problems in SUD treatment High non-response rate Clients’ mixed reactions to “standard care” in the treatment system: Behavioral interventions Group counseling 12-step model (i.e., AA approach) Currently, treatment seekers with substance use disorders (SUD) really do not have many TX options

Treatment as usual Intensive Outpatient Program (IOP) Total of 9 hours of treatment per week, typically spread over 3 days Primarily group counseling and group didactic sessions (e.g., films, lectures) 12-step, abstinence-oriented approach Little to no availability of addiction medication Standard Outpatient (OP) Same as above, 1-2 hours/week

Evidence-based SUD treatments Behavioral Cognitive-Behavioral Therapy (CBT) Motivational Enhancement Therapy (MET) and Motivational Interviewing (MI) Behavioral Couples Therapy (BCT) 12-Step Facilitation Therapy (TSF) Contingency Management (CM) Medications Naltrexone/vivitrol (alcohol/opioids) Methadone/buprenorphine (opioids)

Limits of the evidence base Effects are often relatively small Studies tend not to support predicted moderation and mediation effects As more studies are done, effects tend to get smaller (failure to replicate) In most recent meta-analysis, CBT was no better than other active interventions Therefore, rates of non-response to even the best “evidence-based” treatments are often surprisingly high

Hypothetical example Trial comparing “BestShot” (BS) intervention to standard care (SC) for cocaine dependence Rigorous study design with high follow-up rate and biologically confirmed cocaine outcomes assessed over 12 months finds: BS yields a cocaine abstinence rate that averages 15 percentage points higher than SC across all follow-ups (50% vs. 35%, p< .05) Authors conclude that BS should replace SC But we still have half of the participants in BS failing to achieve cocaine abstinence at any given follow-up– what do we do for them??

Possible solution: Adaptive treatment Develop an algorithm in which: Client progress is monitored using standardized procedures and instruments When non-response is apparent, modify treatment according to a predetermined set of decision rules Methods for developing algorithm Prior research/clinical judgment Experimental procedures

Example Cocaine dependent clients who do not achieve abstinence early in treatment are likely to have bad outcomes– what can be done to help them?? (i.e., what is the best “Plan B”?) Does augmenting standard care with extended recovery support help? Does also providing incentives to attend extended recovery support further improve outcomes by increasing sustained attendance?

Example, cont. Run a trial in which non-responders (i.e., those who continue to drink/use cocaine) are randomized to: Standard care only SC plus augmentation with Telephone Monitoring and Counseling extended recovery support (TMC) SC plus augmentation with extended recovery support plus incentives (TMC+)

Effects of Cocaine Use Early in Treatment Baseline cocaine use p< .0001 TX x cocaine use interaction p< .07 TMC > TAU OR=1.95 p= .04 TMC+ >TAU OR=1.58 p=.14

Effects of Alcohol Use Early in Treatment Alcohol use p< .0001 TX x alcohol use interaction p=.04 TMC > TAU OR=2.47 p= .007 TMC+>TAU OR=1.71 p= .09

Limitations of such studies Rates of non-response may still be too high even in the best TX identified with the first randomization Treatment resistant disorder Moderately effective TX Client response heterogenity Need to identify the best “Plan C” for persistent non-responders

Possible solution! Study design in which non-responders to the first adaptive modification are re-randomized to: Another shot at “Plan B” vs. standard care A new intervention– “Plan C”– vs. standard care or Plan B Entirely new set of interventions– “Plan C vs. Plan D” Referred to as Sequential Multiple Assignment Randomized Trial (SMART)

Using SMART design to address high dropout rate in SUD TX The reality is that almost all available SUD TX is 12-step oriented interventions provided in outpatient programs via group counseling Dropout rate is high– why? Low efficacy Considerable response heterogeneity Many clients do not like this approach What can be done to improve outcomes within this system?

Research questions Does offering patients who do not engage in standard outpatient treatment a choice of other interventions improve outcomes? Does offering patients who initially engage but then drop out a choice of other interventions improve outcomes? Does a second attempt to offer TX choice to persistent non-engagers improve outcomes?

Tailoring Variable We are tailoring on IOP attendance (rather than substance use) Definition of “disengaged” was derived through an expert consensus process At 2 weeks: failure to attend any treatment in the second week following intake During weeks 3-7: failure to attend any IOP sessions for two consecutive weeks At 8 weeks: failure to attend any IOP sessions in prior two weeks

Treatment Sites and Patients Participants recruited from IOPs in publicly funded and VA programs Participants enrolled at intake Two studies: Cocaine dependent (N=300), 80% with past or current alcohol dependence Alcohol dependent (N=200), 40% with past or current cocaine dependence Typical participant: African-American male in Philadelphia, around 40yo

Adaptive Protocol With Patient Choice Week 2 Week 8 Intake to Specialty Care (IOP) Engaged Patients Telephone MI For IOP Engagement Now Engaged Monitor for Self-Selection Two weeks Randomization Still Non-Engaged Non-Engaged Patients Telephone MI With Choice of TX Option Second Randomization TEL MI W/Choice No Further MI Calls CBT Medical Management Stepped Care IOP

Monthly Outcome Measures Alcohol Use (for alcohol dependent Pts) Any use and any heavy use Frequency of any and heavy use Cocaine Use (for cocaine dependent Pts) Any use Frequency of use Urine toxicology

Study Participation Engaged/Disengaged at Week 2: Study 1– 188 (63%) / 112 (37%) of 300 Study 2– 123 (62%) / 77 (38%) of 200 Disengaged Weeks 3-7: Study 1—43 (23%) of 188 engaged at W2 Study 2—24 (20%) of 123 engaged at W2 Still disengaged at Week 8: Study 1—66 (59%) of 112 disengaged W2 Study 2—43 (56%) of 77 disengaged W2

Treatment Options in MI-PC Intensive outpatient program (IOP) Cognitive-behavioral therapy (CBT) Telephone stepped care (telephone) Medication plus medical management (medication)

What non-engaged MI-PC PTs select in weeks 2-7:

What non-engaged MI-PC PTs select at week 8: (at re-randomization)

Alcohol Use in Patients Disengaged at 2 weeks Main Effects Analyses Alcohol Use in Patients Disengaged at 2 weeks

Any Alcohol Use in Month Study 1 Study 2 p= .012 p= .028

Days of Alcohol Use per Week Study 1 Study 2 p= .015 p= .02

Alcohol outcomes in combined sample (N=161 of 428 alc dep) Any drinking: OR= 0.40, p= .0007 Any heavy drinking OR= 0.33, p= .001 Frequency of drinking B= -1.08, p= .009 Frequency of heavy drinking B= -1.09, p= .003 MI-PC= 0, MI-IOP= 1

Patients Not Engaged at 2 Weeks: Rates of Any Heavy Drinking in Each Follow-up Month

Patients Not Engaged at 2 Weeks: Frequency of Heavy Drinking Days in Each Follow-up Month

Alcohol Use in Patients Disengaged between weeks 3 and 7 Main Effects Analyses Alcohol Use in Patients Disengaged between weeks 3 and 7

Disengaged in weeks 3-7 in combined sample (N=73) Any alcohol use OR= 0.54, p= .16 Any heavy alcohol use OR= 0.67, p= .36 Frequency of use B= -0.84, p= .23 Frequency of heavy use B=-1.03, p= .10 MI-PC= 0, MI-IOP= 1

Alcohol Use in Patients Disengaged at both 2 and 8 weeks Main Effects Analyses Alcohol Use in Patients Disengaged at both 2 and 8 weeks

Disengaged at weeks 2 and 8 in combined sample (N=86) Any alcohol use OR= 1.12, p= .79 Any heavy alcohol use OR= 1.43, p= .45 Frequency of use B= -0.34, p= .58 Frequency of heavy use B= 0.02, p= .97 MI-PC= 1, no further outreach=0

Main Effects Analyses Cocaine Use Outcomes

Cocaine use (N= 409) PTs disengaged at w2 (N=159): NS (P values .13 to .86) PTs disengaged in w3-7 (N=69): NS (p values .16 to .74) (results in direction of IOP better than PC) PTs disengaged w2 and w8 (N=84): NS (p values .14 to .42) (results in direction of NFO better than PC)

Moderators Study, site, and dependence status (current vs. past) did not interact with treatment condition in most analyses Exception: In patients re-randomized at 8 weeks, those with past alcohol dependence benefited from MI-PC, whereas those with current dependence did not. Same finding obtained with re-randomized cocaine dependent patients

Conclusions Providing substance dependent patients who fail to engage in IOP a choice of other treatment options does not improve alcohol or cocaine use outcomes In fact, outreach without a choice of other treatments leads to better alcohol use outcomes in those who do not engage in IOP initially

Conclusions No advantage to providing outreach and a choice of interventions to patients who engage initially but then drop out Providing further outreach with a choice of interventions to those not engaged at 2 and 8 weeks did not improve SUD outcomes compared to no further outreach Possible exception: Patients with past rather than current dependence at intake

Encouraging results It is possible to successfully implement a SMART project in SUD patients Use of telephone MI made it possible to at reach most patients after 1st and 2nd randomization, even though they were not engaged in treatment at that point. Significant treatment effects obtained (although in the opposite direction of what was predicted!)

Study limitations Did not consider impact of TX choice at intake Alternative treatments were not provided by IOP staff, and were delivered at a different location We did not offer some possible TX combi-nations (e.g., IOP+meds, meds+CBT, etc.) No TAU control at first randomization, no MI-IOP condition at second randomization

Challenges in SMART trials for Substance Dependence PTs who are doing badly are hard to reach and are often unwilling to participate further in treatment of any sort Identifying non-responders early in the process and delivering modified intervention in a timely fashion Mechanisms of action in behavioral treatment options may not be sufficiently different that PT doing poorly in one treatment will respond to another option Having power to include all relevant comparison conditions at each randomization Still using group average results to develop decision rules for individuals

Exciting new developments Using mobile health communication technology to: Assess clients much more frequently, without becoming a burden to them Make use of passive, as well as active, assessments (e.g., activity level, location) Deliver automated interventions immedi-ately when non-response is detected Conduct SMART studies with many separate randomizations

Funding Support for this study provided by NIH grants: P60 DA05186 (O’Brien, PI) P01 AA016821 (McKay, PI) K24 DA029062 (McKay, PI) RC1 AA019092 (Lynch, PI)

Collaborators Penn Other Institutions Dave Oslin Kevin Lynch Tom Ten Have Debbie Van Horn Michelle Drapkin Other Institutions Susan Murphy, Inbal Nahum-Shani, Danny Almirall, University of Michigan Linda Collins, Penn State

Acknowledgments Our Research Team Oubah Abdalla John Cacciola Rachel Chandler Dominic DePhilippis Michelle Drapkin Ayesha Ferguson Ellen Fritch Jessica Goodman Angela Hackman Dan Herd Laurie Hurson Ray Incmikoski Laura Harmon Megan Long Jen Miles Jessica Olli Zakkiyya Posey Alex Secora Tyrone Thomas Debbie Van Horn Sarah Weiss Tara Zimmerman