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

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Developing Dynamic Treatment Regimes for Chronic Disorders

S.A. Murphy

Univ. of Michigan

RAND: August, 2005

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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

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Goals

• Tomorrow– Four categories of methods for constructing

dynamic treatment regimes using data.– Generalization error

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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)

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Goals




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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.

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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.

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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

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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.

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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

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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?

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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.

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Goals





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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

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Design Considerations

•Choice of the tailoring variable

•Measurement of the tailoring variable

•Decision rules linking tailoring variables to decisions

•Implementation of the decision rules

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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.

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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.

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Technical Interlude!

S=tailoring variablet=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.

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S Interacts with Treatment but is NOT a Tailoring Variable

0 1

Treatment

Y

s=1

s=0

S is a Tailoring Variable

0 1

Treatment

Y

s=1

s=0

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Measurement of tailoring variables

•Reliability – high signal to noise ratio

•Validity – unbiased

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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?”

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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.

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Implementation

•Try to implement decision rules universally, applying them consistently across subjects, time, site & staff members.

•Document values of the tailoring variable!

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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.

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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.

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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)

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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.

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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:[email protected]

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Goals


behavioral/psychosocial theories, clinical experience and expert opinion



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Unknown UnknownCauses Causes

Observed Treatment 1 Observed Treatment 2 ObservedVariables Outcomes Outcomes

Time 2 Time 3

Conceptual Structure

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

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Treatment of Alcohol Dependency

Initial Txt Intermediate Outcome Secondary Txt

TDM +Responder counseling

TDM

Med B

Med ANonresponder

EM + Med B+ Psychosocial

Intensive OutpatientProgram

Responder TDM +counseling

TDM

Med A + Psychosocial Med B

Nonresponder

EM +Med B+Psychosocial

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TDM

Med B

Med ANonresponder




TDM


Nonresponder


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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).

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

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Sequential Multiple Assignment Randomization


TDM +

Responder R counseling

TDM

Med B

Med A

Nonresponder REM + Med B+ Psychosocial

R

Responder TDM +

R counseling

TDM


Nonresponder R


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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.

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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.

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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.

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A Methodological Explanation of Delayed Effects

Suppose nature is your best friend and tells you all you need to know!

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TDM

Med B

Med ANonresponder




TDM


Nonresponder


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TDM

Med B

Med ANonresponder




TDM


Nonresponder


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Summary:

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

treatments.

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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

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Why statistical methods for combining over multiple trials are not always the

answer

• Causal effects of prior treatment and non-causal correlations

• Cohort Effects

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Unknown UnknownCauses Causes

Observed Treatment 1 Observed Treatment 2 ObservedVariables Outcomes Outcomes

Time 2 Time 3

Conceptual Structure

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Causal Effects of Initial Txt

Unknown Causes

Treatment Observed Treatment Observed one Outcome 2 two Outcome 3

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Non-causal Correlations

Unknown Causes

Treatment Observed Treatment Observed one Outcome 2 two Outcome 3

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Cohort Effects

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

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An Aside!

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Medical Decision Making

Disease Yes/No?

Diagnostic Test Results Txt OutcomeTest

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Sequential Experimental Designs

Parameters

Txt Outcome Txt Outcome

Individual 1 Individual 2

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Third Alternate Approach

Why not use theory, clinical experience and expert opinion to construct the dynamic treatment regime and then compare this regime against an appropriate alternative in a confirmatory randomized two group trial?

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Why constructing a dynamic treatment regime and then comparing the regime against a

standard alternative is not always the answer.

• Dynamic treatment regimes are high dimensional multi-component treatments

• We need to address: when to start treatment?, when to alter treatment?, which treatment alteration?, what information to use to make each of the above decisions?

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Multistage Optimization Strategy

MOST

• Open the black box: Screening of candidate program components

• Refining of program components

• Confirming the effectiveness of a refined program

• See Collins et al. “A strategy for optimizing and evaluating behavioral interventions.”

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Meeting the Challenges

Delayed/Cohort Effects: SMART

High Dimensionality: Screening/refining randomized trials prior to a confirmatory trial (MOST).

The SMART design is one of the randomized trials in MOST

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Examples of SMART designs:

•CATIE (2001) Treatment of Psychosis in Alzheimer’s Patients

•CATIE (2001) Treatment of Psychosis in Schizophrenia

•STAR*D (2003) Treatment of Depression

•Thall et al. (2000) Treatment of Prostate Cancer

•Oslin (on-going) Treatment of Alcohol Dependence

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SMART Designing Principles

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TDM

Med B

Med ANonresponder




TDM


Nonresponder


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•At each decision point, restrict class of treatments only by ethical, feasibility or strong scientific considerations. Use a low dimension summary (responder status) instead of all intermediate outcomes (time until nonresponse, adherence, burden, stress level, etc.) to restrict class of treatments.

•Collect intermediate outcomes that might be useful in ascertaining for whom each treatment works best; information that might enter into the decision rules.

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•Choose a primary hypothesis that is both scientifically important and aids in developing the dynamic treatment regime.

•Power trial to address this hypothesis.

•Choose secondary hypotheses that further develop the dynamic treatment regime and use the randomization to eliminate confounding.

•Trial is not necessarily powered to address this hypothesis.

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Primary Hypothesis: Given the secondary treatments provided do dynamic treatment regimes beginning with med A produce different mean drinking scores than dynamic treatment regimes beginning with med A plus psychosocial therapy?

Analysis: Compare estimated mean response between the two groups, one group initially assigned med A to the other group initially assigned med A plus psychosocial therapy.

Response: A summary of drinking behavior over the entire study duration, beginning when initial treatment is offered.

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An analysis that is less useful in the development of dynamic treatment

regimes:

Decide whether initial treatment A is better than initial treatment A + psychotherapy by comparing intermediate outcomes (proportion of immediate responders).

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Secondary Hypothesis: Hypothesize that non-adhering non-responders will have lower drinking if provided a change in medication + EM+ counseling as compared to a change in medication only.

Analysis: Use an analysis that tests if other intermediate outcomes differentiate for whom each secondary treatment is best and if any pretreatment information differentiates for whom each initial treatment is best.

Response: A summary of drinking behavior beginning at the earliest time secondary treatment can be offered.

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How might this work out?

1. Use sample size so as to power the primary analysis with a high type I error (.1) and high power (.9).

2. Suppose best treatment strategy beginning with A + psychotherapy produces approximately the same mean drinking score as the best treatment strategy beginning with A alone.

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3. In secondary analyses, we find that among adherers to initial treatment we detect no difference between EM + med B+ psychosocial versus EM whereas among non-adherers to initial treatment EM + med B+ psychosocial results in a better response than med B.

4. In secondary analyses, we detect no difference between secondary treatments for responders.

Recall study not powered for these secondary analyses.

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In second MOST trial, provide treatment A to all patients. If responder, assign TDM alone versus TDM + telephone counseling. If nonresponder, randomly assign med B versus EM+med B versus EM +med B + psychosocial counseling.

Power second trial to address these (or just second) hypotheses.

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We see no difference in drinking scores between secondary treatments for responders. We confirm the interaction: nonresponding-nonadherers benefit more from EM + med B than med B alone whereas nonresponding-adherers benefit the same amount from EM+ med B as from med B. A comparison of EM+ med B versus EM + med B + psychosocial indicates no benefit of psychosocial counseling above and beyond EM+ med B.

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Final MOST trial: confirmatory trial comparing two groups.

1) Treatment Regime: Assign med A initially. If responder provide TDM; if nonresponder-nonadherer assign EM+med B; if nonresponder-adherer assign med B.

Versus

2) Standard Care.

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Note:

If the SMART experimental design is unbalanced (e.g. the number of treatment options vary by past treatment or by prior observations) then the sample size formulae and analysis become more complicated.

See paper in Stat. in Medicine at http://www.stat.lsa.umich.edu/~samurphy/papers/ExperimentalEvidence.pdf

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Goals


behavioral/psychosocial theories, clinical experience and expert opinion



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Feedback

• Do you anticipate objections to – SMART?– MOST?

• Do you anticipate power issues with SMART?• Other approaches to experimentally develop

dynamic treatment regimes?– Fractional factorials?– Sequential experimental design a la Berry and

colleagues?

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This work is based on the papers;

Murphy, Stat. in Medicine (2005) at http://www.stat.lsa.umich.edu/~samurphy/papers/ ExperimentalEvidence.pdf

And the Murphy, Lynch, McKay, Oslin, Ten Have paper at http://www.stat.lsa.umich.edu/~samurphy/papers/SMART.pdf (this is a technical report)

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This seminar can be found at:http://www.stat.lsa.umich.edu/~samurphy/seminars/

My email address:

[email protected]

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

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