Smart designs for developing dynamic treatment regimes
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SMART Designs for Developing Dynamic Treatment Regimes. S.A. Murphy Symposium on Causal Inference Johns Hopkins, January, 2006. Outline. Why Dynamic Treatment Regimes? Why SMART experimental designs? Design Principles and Analysis of Balanced Designs.

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SMART Designs for Developing Dynamic Treatment Regimes

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Smart designs for developing dynamic treatment regimes

SMART Designs for Developing Dynamic Treatment Regimes

S.A. Murphy

Symposium on Causal Inference

Johns Hopkins, January, 2006


Outline

Outline

  • Why Dynamic Treatment Regimes?

  • Why SMART experimental designs?

  • Design Principles and Analysis of Balanced Designs


Smart designs for developing dynamic treatment regimes

  • Dynamic treatment regimes are individually tailored treatments, with treatment type and dosage changing according to patient outcomes. Operationalize clinical practice.

  • Brooner et al. (2002) Treatment of Cocaine Addiction

  • Breslin et al. (1999) Treatment of Alcohol Addiction

  • Prokaska et al. (2001) Treatment of Tobacco Addiction

  • Rush et al. (2003) Treatment of Depression


Why dynamic treatment regimes

Why Dynamic Treatment Regimes?

  • In Prevention

    • Individuals are at risk of problem behaviors for different reasons (multiple causes of the problem behavior)

    • Periods of high and low risk

    • Periods of high and low need for treatment

    • Some individuals may have already exhibited early problems


Why dynamic treatment regimes1

Why Dynamic Treatment Regimes?

  • In Treatment

    • High heterogeneity in response to any one treatment

      • What works for one person may not work for another

      • What works now for a person may not work later

    • Improvement often marred by relapse

    • Intervals during which more intense treatment is required alternate with intervals in which less treatment is sufficient

    • Co-occurring disorders may be common


Smart designs for developing dynamic treatment regimes

  • 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.

  • Problems with adherence:

    • Variations of treatment or different delivery mechanisms may increase adherence

    • Excessive treatment may lead to non-adherence

  • Treatment is costly (Would like to devote additional resources to patients with more severe problems)

  • More is not always better!


Smart designs for developing dynamic treatment regimes

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 and is nonadherent then the patient’s medication is augmented by CBI. If the patient experiences 2 or more heavy drinking days during this period and is adherent then the patient’s medication is switched to acamprosate.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.


Smart designs for developing dynamic treatment regimes

  • 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 decisions?


Smart designs for developing dynamic treatment regimes

Why SMART Trials?

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

These are multi-stage trials; conceptually a randomization takes place at each stage.

Goal is to inform the construction of a dynamic treatment regime.


Smart designs for developing dynamic treatment regimes

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.


Smart designs for developing dynamic treatment regimes

Delayed Effects

Negative synergies: An initial treatment may produce a higher proportion of responders but also result in 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.


Smart designs for developing dynamic treatment regimes

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.


Smart designs for developing dynamic treatment regimes

Summary:

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


Smart designs for developing dynamic treatment regimes

Second Alternate Approach to SMART

  • Why not use data from multiple randomized 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 randomized trials.

      • Methods from Medical Decision Making involving a variation of a Markovian assumption


Smart designs for developing dynamic treatment regimes

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

  • Cohort Effects

  • Causal effects of prior treatment and non-causal correlations


Smart designs for developing dynamic treatment regimes

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.


Smart designs for developing dynamic treatment regimes

  • Summary:

  • Standard randomized trials may yield information about different populations from SMART trials.

  • Causal models are often incomplete resulting in difficulties in comparing initial treatments that are to be part of the dynamic treatment regime.


Smart designs for developing dynamic treatment regimes

Third Alternate Approach to SMART

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 trial?

The alternative may be the same regime but with one component altered.


Smart designs for developing dynamic treatment regimes

  • Problems with the two group trials (or repeated cycles of randomized two group trials)

  • Dynamic treatment regimes are multi-component treatments:

    • when to start treatment?, when to alter treatment?, which treatment is best next?, what information to use to make each of the above decisions?

    • We are not opening the black box— we don’t know why we get or do not get significance and

    • Heavy reliance on expert opinion or best guesses --to choose not only the components but the level of these components.


Smart designs for developing dynamic treatment regimes

Problems with the two group comparison (or repeated cycles of randomized two group trials)

  • Results may not replicate well --miss interactions,

  • Takes a long time to “optimize” the multi-component treatment --method depends on no interactions and

  • Some components are costly --retain costly, inactive components


Smart designs for developing dynamic treatment regimes

  • 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


Smart designs for developing dynamic treatment regimes

SMART Designing Principles


Smart designs for developing dynamic treatment regimes

  • SMART Designing Principles

  • KEEP IT SIMPLE: 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 next treatments.

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


Smart designs for developing dynamic treatment regimes

  • SMART Designing Principles

  • Choose primary hypotheses that are both scientifically important and aid in developing the dynamic treatment regime.

    • Power trial to address these hypotheses.

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

    • Trial is not necessarily powered to address these hypotheses.


Smart designs for developing dynamic treatment regimes

  • SMART Designing Principles:

  • Primary Hypothesis

  • EXAMPLE 1: (sample size is highly constrained): Hypothesize that given the secondary treatments provided, the initial treatment Med A + psychosocial counseling leads to lower drinking than the initial treatment Med A alone.

  • EXAMPLE 2: (sample size is less constrained): Hypothesize that a particular dynamic treatment regime beginning with Med A+ psychosocial counseling results in lower drinking than a particular dynamic treatment regime beginning with Med A.


Smart designs for developing dynamic treatment regimes

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

  • Decide whether med A is better than med A + psychosocial counseling by comparing intermediate outcomes (proportion of immediate responders).


Smart designs for developing dynamic treatment regimes

  • SMART Designing Principles

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

  • EXAMPLE: 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.


Discussion

Discussion

  • Secondary analyses can use pretreatment variables and outcomes to provide evidence for a more sophisticated dynamic treatment regime. (when and for whom?)

  • SMART design and analyses targeted at scientific goal of informing the construction of a high quality dynamic treatment regime

  • Adherence is not a nuisance; adherence indicates need to tailor treatment.


Smart designs for developing dynamic treatment regimes

This seminar can be found at:

http://www.stat.lsa.umich.edu/~samurphy/

seminars/Hopkins0106.ppt

This seminar is partially based on a paper with Kevin Lynch, Jim McKay, David Oslin and Tom Ten Have. Email me with questions or if you would like a copy:

[email protected]


Smart designs for developing dynamic treatment regimes

Conceptual Structure


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