1 Meeting the Future in Managing Chronic Disorders: Individually Tailored Strategies S.A. Murphy Herbert E. Robbins Collegiate Professorship in Statistics Lecture November 14, 2006

2 Outline –Three apparently dissimilar problems –Myopic decision making –Constructing strategies –Challenges Unknown, unobserved causes Small, expensive data sets –Discussion

3 Three Apparently Dissimilar Problems –Artificial Intelligence: Autonomous Helicopter Flight –Management of Chronic Mental Illnesses –Management of a Welfare Program

4 Artificial Intelligence Autonomous Helicopter Flight –Observations: characteristics of the helicopter (position, orientation, velocity, angular velocity, ….), characteristics of the environment (wind speed, wind angle, turbulence….) –Actions/treatments: cyclic pitch (causes forward/backward and sideways acceleration), tilt angle of main rotor blades (direction), tail rotor pitch control (turning) –Rewards: Closeness of helicopter’s flight path to the desired path; avoidance of crashes(!)

5 Andrew Ng’s Helicopter:

6 The Management of Chronic Mental Illnesses Treating Patients with Opioid Dependence (heroin) –Observations: individual characteristics (withdrawal symptoms, craving, attendance at counseling sessions, results of urine tests….), characteristics of the environment (housing, employment.…) –Actions/treatments: methadone dose, amount of weekly group counseling sessions, daily dosing time of methadone, individual counseling sessions, methadone taper –Rewards: minimize opioid use and maximize health/functionality, minimize cost

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8 Management of a Welfare Program “Jobs First” Program in Connecticut –Observations: individual characteristics (assets, income, age, health, employment), characteristics of the environment (domestic violence, incapacitated family member, # children, living arrangements…) –Actions/treatments: child care, job search skills training, amount of cash benefit, medical assistance, education –Rewards: maximize employment/independence.

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10 The Common Thread: Multi-Stage Decision Making Observation, action, observation, action, observation, action,……………………. A strategy tells us how to use the observations to choose the actions. We’d like to develop strategies that maximize the rewards.

11 Myopic Decision Making

12 Myopic Decision Making In myopic decision making, decision makers use strategies that seek to maximize immediate rewards. Problems: –Ignore longer term consequences of present actions. –Ignore the range of feasible future actions/treatments –Ignore the fact that immediate responses to present actions may yield information that pinpoints best future actions (A strategy tells us how to use the observations to choose the actions.)

13 Autonomous Helicopter Flight The helicopter has veered from flight plan. Myopic action: Choose an acceleration and direction that will ASAP bring us back to the flight plan. The result: The myopic action results in the helicopter overshooting the planned flight path and in drastic situations may lead to the helicopter cycling out of control. The mistake: We did not consider the range of actions we can take following the initial action. The ability to slow down is mechanically limited. The message: Use an acceleration that will not return us as quickly to the planned flight path but will take into account the ability of the helicopter to slow down and reduce the overshoot.

14 Treatment of Psychosis Myopic action: Choose a treatment that reduces psychosis for as many people as possible. The result: Some patients are not helped and/or experience abnormal movements of the voluntary muscles (TDs). The class of subsequent medications is greatly reduced. The mistake: We should have taken into account the variety of treatments available to those for whom the first treatment is ineffective. The message: Use an initial medication that may not have as large a success rate but that will be less likely to cause TDs.

15 Treatment of Opioid Dependence Myopic action: Choose an intensive multi-component treatment (methadone + counseling + behavioral contingencies) that immediately reduces opioid use for as many people as possible. The result: Behavioral contingencies are burdensome/expensive to implement and many people may not need the contingencies to improve. The mistake: We should allow the patient to exhibit poor adherence prior to implementing the (negative) behavioral contingencies. The message: Use an initial treatment that may not have as large an immediate success rate but carefully monitor patient adherence to ascertain if behavioral contingencies are required.

16 Constructing Strategies

17 Basic Idea for Constructing a Strategy: Move Backwards Through Time. (Pretend you know the distribution of all outcomes!)

18 Challenges

19 Goal Combine theory and data in a principled fashion to construct a good strategy. In A.I. scientists combine mechanistic theories with data from experiments to construct strategies that maximize the rewards.

20 Artificial Intelligence Scientists who construct strategies in autonomous helicopter flight use mechanistic theory (physical laws: momentum=m*v, W=F*d*cos(θ)…) to model the interrelationships between observations. –Scientists know many (most?) of the causes of the observations and know how the observations relate to one another. Scientists can quickly evaluate the strategies (within a matter of months).

21 Comparatively Less Known Mechanistic Models in Behavioral/Social/Medical Sciences Scientists who want to use data on individuals to construct treatment strategies must confront the fact that non-causal “associations” occur due to the unknown causes of the observations.

22 Conceptual Structure in the Behavioral/Social/Medical Sciences

23 Unknown, Unobserved Causes (Incomplete Mechanistic Models)

24 Unknown, Unobserved Causes (Incomplete Mechanistic Models) Problem: Non-causal associations between treatments (here counseling) and rewards are likely. Solution: Construct strategies using data sets in which randomization is used to assign treatments to students. This breaks the non-causal associations yet permits causal associations.

25 Unknown, Unobserved Causes (Incomplete Mechanistic Models)

26 Unknown, Unobserved Causes (Constructing Sequences of Treatment)

27 Unknown, Unobserved Causes (Incomplete Mechanistic Models)

28 Unknown, Unobserved Causes (Incomplete Mechanistic Models)

29 The problem: Even when treatments are randomized, non-causal associations occur in the data. The solution: Statistical methods for constructing strategies must be conducted in stages as opposed to “all-at-once.” Statistical methods should appropriately “average” over the non-causal associations between treatment and reward. Unknown, Unobserved Causes (Incomplete Mechanistic Models)

30 Summary of Solutions To Causal Problems Subjects in your data should be representative of population of subjects. Experiments should randomize actions. Develop statistical methods that avoid being influenced by non-causal associations yet help you construct the strategy.

31 Expensive Data on a Limited Number of Individuals Scientists who want to use data on individuals to construct treatment strategies must provide measures of confidence and also evaluations of alternative treatment strategies. Methods for constructing strategies are non- smooth.

32 Basic Idea for Constructing a Strategy: Move Backwards Through Time.

33 Expensive, Limited Data on Individuals In order to provide measures of confidence and comparisons of strategies, the statistical methods for constructing strategies must be regularized. A number of statisticians are working hard on this open question. This problem will be one of the foci of the SAMSI program in June, 2007:

34 Some Experiments

35 ExTENd Ongoing study at U. Pennsylvania (D. Oslin) Goal is to learn how best to help alcohol dependent individuals reduce alcohol consumption.

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37 Adaptive Treatment for ADHD Ongoing study at the State U. of NY at Buffalo (B. Pelham) Goal is to learn how best to help children with ADHD improve functioning at home and school.

38 ADHD Study B. Begin low dose medication 8 weeks Assess- Adequate response? B1. Continue, reassess monthly; randomize if deteriorate B2. Increase dose of medication with monthly changes as needed Random assignment: B3. Add behavioral treatment; medication dose remains stable but intensity of bemod may increase with adaptive modifications based on impairment No A. Begin low-intensity behavior modification 8 weeks Assess- Adequate response? A1. Continue, reassess monthly; randomize if deteriorate A2. Add medication; bemod remains stable but medication dose may vary Random assignment: A3. Increase intensity of bemod with adaptive modifi- cations based on impairment Yes No Random assignment:

39 STAR*D This trial is over and the data is being analyzed (PI: J. Rush). One goal of the trial is construct good treatment sequences for patients suffering from treatment resistant depression.

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41 Discussion The best management of chronic disorders (poverty, mental illness, other medical conditions) requires multi-stage decision making. Avoid myopic decision making! –Allow for longer term effects of the treatment –When comparing treatment options take into account the effect of future treatments –Appreciate the value of observing patients outcomes such as adherence Experimental designs and statistical methods are available.

42 This seminar can be found at:

43 Role of the Statistician What kinds of data are most useful for developing strategies? How do we use limited and expensive data to construct good strategies? How do we evaluate strategies using the limited data? (A strategy tells us how to use the observations to choose the actions.)

44 Unknown, Unobserved Causes

45 Unknown, Unobserved Causes

46 Unknown, Unobserved Causes Problem: We recruit students via flyers posted in dormitories. Associations between observations and rewards are highly likely to be (due to the unknown causes) non- representative. Solution: Sample a representative group of college students.