An experimental paradigm for developing adaptive treatment strategies
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An Experimental Paradigm for Developing Adaptive Treatment Strategies. S.A. Murphy NIDA Meeting on Treatment and Recovery Processes January, 2004. Setting : Management of chronic, relapsing disorders such as alcohol addiction, substance abuse and mental illness Characteristics:

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An Experimental Paradigm for Developing Adaptive Treatment Strategies

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An Experimental Paradigm for Developing Adaptive Treatment Strategies

S.A. Murphy

NIDA Meeting on Treatment and Recovery Processes

January, 2004


  • Setting: Management of chronic, relapsing disorders such as alcohol addiction, substance abuse and mental illness

  • Characteristics:

  • May need a sequence of treatments prior to improvement

  • Improvement marred by relapse

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


  • Adaptive Treatment Strategies are individually tailored treatments, with treatment type and dosage changing with ongoing subject need. 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


GOAL: Provide experimental paradigm for developing treatment decision rules.


  • We need an experimental paradigm that will help us answer:

  • When to start treatment?

  • Which treatment to start and for whom?

  • When to step-up treatment?

  • Which step-up treatment and for whom?

  • When to step down treatment to maintenance/monitoring?

  • Which maintenance/monitoring treatment and for whom?

  • What information to use to make each of the above decisions?


EXAMPLE: Treatment of alcohol dependency. Primary outcome is a summary of heavy drinking scores over time


GOAL: Design trials that have the goal of developing treatment decision rules leading to a minimization of the mean response, (mean drinking score over time).

PROPOSAL: Sequential within-person randomization: Randomize at each decision point.


  • Why randomize subjects multiple times?

  • Initial treatments are best compared in the context of available secondary treatments.

    • Initial treatment may have delayed effects.

    • Initial treatment may work together with a particular secondary treatment to lead to an enhanced effect.

  • Assess best sequencing of treatments.


  • Examples of sequentially within-person randomized trials:

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

  • CATIE (2001) Treatment of Psychosis in Schizophrenia

  • STAR*D (2001) Treatment of Depression

  • Thall et al. (2001) Treatment of Prostate Cancer


  • Principles in Designing a Sequentially Within-Person Randomized Trial

  • Secondary treatment alternatives should vary by only a simple low dimension summary (responder status) instead of all intermediate outcomes (adherence, burden, craving, etc.).

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


  • Principles in Designing a Sequentially Within-Person Randomized Trial

  • Choose a primary hypothesis that is both scientifically interesting and aids in the development of the adaptive treatment strategy.

  • Choose secondary hypotheses that further develop the adaptive treatment strategy and use the randomization to reduce confounding.


Proposal

  • Primary Analysis: discriminate between strategies with different initial treatments.

  • In primary analysis consider only simple adaptive treatment strategies with decision rules depending only on summaries of intermediate outcomes (responder/nonresponder)

  • Use a weighted regression analysis.


Proposal

  • Secondary analyses: consider more complex adaptive treatment strategies with decision rules depending on intermediate outcomes.

  • Test if other intermediate outcomes differentiate for whom each future treatment is best and if any pretreatment information differentiates for whom each initial treatment is best. (Murphy, 2003; Robins, 2003)


  • An analysis that is less useful in the development of adaptive treatment strategies!

  • Decide whether initial treatment A is better than initial treatment B by comparing intermediate outcomes (responder status).


  • Two Challenges

  • How do we use high dimensional information to improve decision making?

  • Many potential treatment components in an adaptive strategy: how do we discover which are active and if there are unexpected negative interactions?


The paper can be found at

http://www.stat.lsa.umich.edu/~samurphy/papers/ExperimentalEvidence.pdf


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