Developing Dynamic Treatment Regimes for Chronic Disorders

1. Developing Dynamic Treatment Regimes for Chronic Disorders S.A. Murphy Univ. of Michigan RAND: August, 2005

2. Goals • Today • Dynamic Treatment Regimes • Designing a dynamic treatment regime using behavioral/psychosocial theory, clinical experience and expert opinion • Experiments that can be used to assist in the construction of dynamic treatment regimes • Feedback on experimental design ideas

3. Goals • Tomorrow • Four categories of methods for constructing dynamic treatment regimes using data. • Generalization error

4. Collaborators • Linda Collins (Health & Human Development) • Karen Bierman (Psychology) • David Oslin (Psychiatry) • John Rush (Psychiatry) • Jim McKay (Psychiatry) • James Robins (Biostatistics & Epidemiology) • Ji Zhu (Statistics) • Derek Bingham (Statistics) • Victor Strecher (Cancer Center) • Vijay Nair (Statistics) • Tom Ten Have (Biostatistics)

5. Goals • Today • Dynamic Treatment Regimes • Designing a dynamic treatment regime using behavioral/psychosocial theory, clinical experience and expert opinion • Experiments that can be used to assist in the construction of dynamic treatment regimes

6. Challenges in managing the chronic forms of addiction disorders, mental illnesses and HIV • High variability across patients in response to any one treatment • No Cure • Relapse is likely without either continuous or intermittent treatment for a large proportion of people.

7. Challenges in managing the chronic forms of addiction disorders, mental illnesses and HIV • What works now may not work later • Exacerbations in disorder may occur if there are no alterations in treatment • Co-occurring disorders are not uncommon.

8. Dynamic Treatment Regimes are individually tailored treatments, with treatment type and dosage changing with subject outcomes. Mimic Clinical Practice. • Brooner et al. (2002) Treatment of Opioid Addiction • Breslin et al. (1999) Treatment of Alcohol Addiction • Prokaska et al. (2001) Treatment of Tobacco Addiction • Rush et al. (2003) Treatment of Depression

9. Example of a Dynamic Treatment Regime Treatment of alcohol dependence. Goal is to reduce drinking. Following graduation from the intensive outpatient program the patient is prescribed naltrexone. The patient is monitored weekly over the next two months. If the patient experiences 2 or more heavy drinking days during this period then the patient’s medication is augmented by CBI. If the patient is able to make the entire 2 months with 1 or no heavy drinking days then the patient is continued on naltrexone and the patient is provided telephone disease management.

10. Components of a dynamic treatment regime • Tailoring Variables (which ones and how to measure?) • Decisions (what are the options at this time?) • Decision Rules (input the tailoring variables and output a decision) one per key decision • A dynamic treatment regime is a sequence of decision rules that input tailoring variables and output recommended decisions

11. The Big Questions • What is the best sequencing of treatments? • What is the best timings of alterations in treatments? • What information do we use to make these treatments?

12. Why not combine all possible efficacious therapies and provide all of these to patient now and in the future? • Treatment incurs side effects and substantial burden, particularly over longer time periods. • Treatment is costly. • Patients are heterogeneous in their need for treatment (across patients and within a patient across time) • Problems with adherence: • Variations of treatment or different delivery mechanisms may increase adherence • Excessive treatment may lead to non-adherence • Need salience • Would like to devote additional resources to patients with more severe disorders.

13. Goals • Today • Dynamic Treatment Regimes • Designing a dynamic treatment regime using behavioral/psychosocial theory, clinical experience and expert opinion • Experiments that can be used to assist in the construction of dynamic treatment regimes • Feedback on experimental design ideas

14. Design Goals • Maximize strength of dynamic treatment regime • By well chosen tailoring variables, well measured tailoring variables & well conceived decision rules. • Maximize replicability in future experimental and real-world implementation conditions • By clearly defining the regime & by fidelity in implementation

15. Design Considerations • Choice of the tailoring variable • Measurement of the tailoring variable • Decision rules linking tailoring variables to decisions • Implementation of the decision rules

16. Choice of Tailoring Variables • Need significant differences in the size of the treatment effect as a function of the tailoring variable • That is, some values of the tailoring variable should indicate a particular treatment decision is best while other values of the tailoring variable should indicate that a different treatment decision is best.

17. Example: Treatment of alcohol dependence • Goal is to reduce drinking; patients who return to drinking need additional or alternate treatment; patients who are not drinking need to be monitored due to high relapse rates. • Tailoring variable is “days heavy drinking” • Providing CBI to patients who are doing well is costly.

18. Technical Interlude! S=tailoring variable t=treatment type (0 or 1) Y=key outcome If is zero or negative for some S and positive for others then S is a tailoring variable.

20. Measurement of tailoring variables • Reliability – high signal to noise ratio • Validity – unbiased

21. Derivation of the decision rules • Articulate a theoretical model for how treatment effect on key outcomes should differ across values of the tailoring variable • Use scientific theory and prior clinical experience • Use prior experimental and observational studies • Discuss with research team and clinical staff, “What treatment option is best for patients with this value on the tailoring variable?”

22. Derivation of decision rules • Good decision rules are objective and are operationalized. • Strive for comprehensive rules (this is hard!) – cover situations that can occur in practice, including when the tailoring variable is unavailable.

23. Implementation • Try to implement decision rules universally, applying them consistently across subjects, time, site & staff members. • Document values of the tailoring variable!

24. Implementation • Exceptions to the rules should be made only after group discussions and with group agreement. • If it is necessary to make an exception, document this so you can describe the implemented treatment.

25. Discussion • Research is needed to build a theoretical literature that can provide guidance: • in identifying tailoring variables • in the development of reliable, and valid indices of the tailoring variables that can be used in the course of repeated clinical assessments.

26. Discussion • Given a structural model of the causal chain relating the tailoring variables, decisions and outcome, statistical methods can help construct the decision rules • Influence diagrams and graphical models (-way to efficiently encode expert knowledge- R. Shachter, S. Lauritzen)

27. Discussion • Research is needed on how we might use existing experimental and observational studies: • in identifying tailoring variables • in constructing best decision rules • Research is needed on how we might design experiments that find good tailoring variables and construct decision rules.

28. Time for a break! The Collins, Murphy, Bierman paper with more details can be found at http://www.stat.lsa.umich.edu/~samurphy/papers/ conceptual.pdf (this paper appeared in Prevention Science) This seminar can be found at: http://www.stat.lsa.umich.edu/~samurphy/seminars/ My email address: samurphy@umich.edu

29. Goals • Today • Dynamic Treatment Regimes • Designing a dynamic treatment regime using behavioral/psychosocial theories, clinical experience and expert opinion • Experiments that can be used to assist in the construction of dynamic treatment regimes • Feedback on experimental design ideas

30. Conceptual Structure

31. EXAMPLE: Treatment of alcohol dependency. Primary outcome is a summary of heavy drinking scores over time. Focus on two key decisions.

34. The Challenges • Delayed Effects & Cohort Effects • ---sequential multiple assignment randomized trials (SMART) • Dynamic Treatment Regimes are Multi-component Treatments • ---series of screening/refining randomized trials prior to confirmatory trial (MOST).

35. What is a sequential multiple assignment randomized trial (SMART)?

37. First Alternate Approach • Why not use data from multiple trials to construct the dynamic treatment regime? • Choose the best initial treatment on the basis of a randomized trial of initial treatments and choose the best secondary treatment on the basis of a randomized trial of secondary treatments.

38. Delayed Effects Negative synergies: An initial treatment may produce a higher proportion of responders but also produce side effects that reduce the effectiveness of subsequent treatments for those that do not respond. Or the burden imposed by this initial treatment may be sufficiently high so that nonresponders are less likely to adhere to subsequent treatments.

39. Delayed Effects Positive synergies: A treatment may not appear best initially but may have enhanced long term effectiveness when followed by a particular maintenance treatment. Or the initial treatment may lay the foundation for an enhanced effect of subsequent treatments.

40. A Methodological Explanation of Delayed Effects Suppose nature is your best friend and tells you all you need to know!

43. Summary: When evaluating and comparing initial treatments we need to take into account the effects of the secondary treatments.

44. Second Alternate Approach • Why not use data from multiple trials to construct the dynamic treatment regime? • Use statistical methods that incorporate the potential for delayed effects and are suited for combining data from multiple trials. • Methods from Medical Decision Making involving a variation of a Markovian assumption

45. Why statistical methods for combining over multiple trials are not always the answer • Causal effects of prior treatment and non-causal correlations • Cohort Effects

46. Conceptual Structure

49. Cohort Effects Subjects who will enroll in, who remain in orwho are adherent in the trial of the initial treatments may be quite different from the subjects in SMART.

50. An Aside!