An Experimental Paradigm for Developing Adaptive Treatment Strategies S.A. Murphy Univ. of Michigan UNC: November, 2003

Adaptive Treatment Strategies

Setting: Management of chronic, relapsing disorders such as alcohol, cocaine addiction 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 Unützer et al. (2001) Treatment of Depression

GOAL : Provide experimental methods for developing treatment assignment, i.e. decision, rules.

k Decisions Observations made prior to j th decision Action at j th decision Summary Response: for a known function u

An adaptive treatment strategy is a vector of decision rules, one per decision If the strategy is implemented then for j=1,…., k.

EXAMPLE : Treatment of alcohol dependency. Response is a summary of heavy drinking scores over time GOAL : How do we design trials so as to develop decision rules that minimize the mean response, mean of summarized drinking score?

Challenges

Two Challenges Delayed Effects ---sequential within-person randomization Adaptive Treatment Strategies are High Dimensional Multi-component Treatments ---series of randomized developmental trials prior to confirmatory trial.

Delayed Effects Or, why choosing the best initial treatment on the basis of a randomized trial of initial treatments and choosing the best secondary treatment on the basis of a randomized trial of secondary treatments will not provide the best adaptive treatment strategy.

Summary: Determining the best initial treatment requires that we first calculate the mean responses of patients for each combination of secondary treatment, intermediate outcome and initial treatment. The main point : If the mean responses to secondary treatment vary by initial treatment then we need to use sequential within- person randomization.

When would the mean response to each combination of secondary treatment, intermediate outcome and initial treatment vary by initial treatment?

Delayed Effects: The Bottom Line Are there unobserved but potentially important common causes of the response and intermediate outcome? Are there unobserved but potentially important causal pathways from initial treatment to final response? If yes to either of the above then use sequentially within-person randomized trials to develop good adaptive treatment strategies.

Adaptive Treatment Strategies are High Dimensional Multi-Component Treatments when to start treatment? which treatment to start? when to step-up treatment? which step-up treatment? when to step down treatment to maintenance/monitoring? which maintenance/monitoring treatment? what information to use to make each of the above decisions?

Meeting the Challenges Delayed Effects: Sequential within-person randomization: Randomize at each decision point. High Dimensionality: Series of developmental randomized trials prior to a confirmatory trial (Box, Hunter and Hunter,1978, pg. 303).

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

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.

Principles in Designing a Sequentially Within-Person Randomized Trial Choose a primary hypothesis that is both scientifically interesting and aids in developing the adaptive treatment strategy. Choose secondary hypotheses that further develop the adaptive treatment strategy and use the randomization to eliminate confounding.

Test Statistic and Sample Size Formula for Primary Analysis

Proposal Primary analysis in developmental trial is to discriminate between strategies with different initial treatments. In primary analysis consider adaptive treatment strategies with decision rules depending only on summaries of X j ’s (say S j ’s) In secondary analyses consider adaptive treatment strategies with decision rules depending on the Xj’s. Choose randomization probabilities to equalize the sample size across possible strategies.

Analyses that do not aid in the development of adaptive treatment strategies! 1)Decide whether initial treatment A is better than initial treatment B by comparing intermediate outcomes (responder status). 2)Decide whether initial treatment A is better than initial treatment B by comparing mean response ignoring the secondary treatments.

Proposal Estimate the mean of Y when the decision rules, are followed: The variance of the estimator is used to construct a sample size formula.

Randomization probability of treatment A j =a j given past: Estimating Function:

Solve, We obtain:

Primary Analysis: Test statistic to compare two strategies with different initial treatments: See Murphy, van der Laan and Robins (2001) for technical details.

Calculating Sample Sizes:

In this example: Balanced design hence choosing the randomization probabilities to equalize the sample size across all possible strategies yields uniform randomization probabilities. An estimator of the mean of Y under the decision rules is the average response of individuals whose treatment pattern is consistent with the rules:

The average response for individuals whose treatment pattern is consistent with the rules: is the number of treatment alternatives at decision j is response variance under treatment strategy

Sample Size Formula: whereis the Type I error and is the power of the test to detect a difference in the mean response between strategies. In our simple example:

Secondary Hypotheses Compare adaptive treatment strategies that begin with the same treatment; in this example, compare response to secondary treatments by levels of the summary intermediate outcome. 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. (Murphy, 2003; Robins, 2003)

Discussion Simulations indicate that sample size formula is accurate for balanced designs. Secondary analyses can only explore adaptive treatment strategies that comply with the restrictions imposed by the experimental design. Trial design and analyses targeted at scientific goal.

Open Problems This setting requires development/generalization of Box's experimentation approach of several developmental trials, all based on randomization prior to a confirmatory trial. How could one use working assumptions on the form of delayed effects to speed up the developmental process? Use working assumptions to pool information. What kinds of working assumptions make sense? How do you detect potential violations of the working assumptions?

Open Problems What is the impact of decisions being made at different times? Choosing randomization probabilities in unbalanced designs. Dealing with high dimensional X-- feature extraction--in secondary analyses.

This seminar can be found at ppt The paper can be found at alEvidence.pdf