Experiments and Adaptive Treatment Strategies S.A. Murphy Univ. of Michigan Chicago: May, 2005

Outline Adaptive Treatment Strategies Challenges in Experimentation Defining Effects Discussion

Adaptive Treatment Strategies

Why does the management of a chronic disorder such as drug dependence require multi-stage decisions? 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. What works now may not work later Exacerbations in disorder may occur if there are no alterations in treatment

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

High-Level Questions What is the best sequencing of therapies? What is the best timings of alterations in therapies? What information do we use to make these decisions?

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

k Decisions on one individual Observation made prior to j th decision point Treatment at j th decision point Primary outcome Y is a specified summary of decisions and observations

Long Term Goal : Construct decision rules that input data at each decision point and output a recommended treatment; these decision rules should lead to a maximal mean Y. where eachis a low dimensional summary of for j=1,…., k In future we choose treatment j using the decision rule

An example of a decision rule is: alter treatment if otherwise maintain on current treatment.

An adaptive treatment strategy is a vector of decision rules, one per decision

Conceptual Structure

Challenges in Experimentation

Two Challenges in Experimentation Delayed Effects ---sequential multiple assignment randomized trials (SMART) Adaptive Treatment Strategies are High Dimensional Multi-component Treatments ---series of developmental, randomized trials prior to confirmatory trial (MOST).

Why a SMART design? 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 is not the best way to construct an adaptive treatment strategy.

Delayed Effects Negative synergies between successive treatments. 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.

Delayed Effects Positive synergies between successive treatments. An initial treatment may not appear best initially but may lay the foundation for an enhanced effect of subsequent treatments. Or a treatment may not appear best initially but may have enhanced long term effectiveness when followed by a particular maintenance treatment.

Examples of sequential multiple assignment randomized trials: CATIE (2001) Treatment of Psychosis in Alzheimer’s Patients CATIE (2001) Treatment of Psychosis in Schizophrenia STAR*D (2003) Treatment of Depression Oslin (ongoing) Treatment of Alcoholism

Defining Effects

Experimental Design Adaptive Treatment Strategies are multi-component treatments Multiple decision points through time Different kinds of decisions Decision options for improving patients are often different from decision options for non-improving patients Services Research Delivery Mechanisms, Encouragement-to-adhere Training of Staff…….

An additional challenge to developing experiments for constructing decision rules. Scarce resources relative to number of interesting treatment components/factors. Implementing many cells of a full factorial is very expensive. Consider designs that are similar to fractional factorials. To do this you must define the effects.

Proposal Two decisions (two stages): Define effects involving T 2 in an ANOVA decomposition of

Conceptual Structure

Proposal Define effects involving T 2 in an ANOVA decomposition of Define effects involving only T 1 in an ANOVA decomposition of

Why uniform? Define effects involving only T 1 in an ANOVA decomposition of If T 2 is uniformly distributed then Orthogonal, mean zero, factors

Interpretability If R is identically 1 and are independently, uniformly distributed then the proposal is equivalent to defining both stage 2 and stage 1 effects in an ANOVA decomposition of T 2 denotes the treatment options when R=1.

Ideally you’d like to replace by (O is a vector of intermediate outcomes) in

Proposal Define effects involving T 2 in an ANOVA decomposition of Define effects involving only T 1 in an ANOVA decomposition of

Use an ANOVA-like decomposition: Proposal

In this case so the stage 1 and stage 2 effects that we are interested in are contained in the same decomposition.

To define effects of factors at first and second decisions use the ANOVA-like decomposition: We are interested in the effects: To design an experiment we make assumptions concerning the negligibility of these effects. Review

Why not use a standard ANOVA decomposition to define effects? because then

Discussion

Statistical Challenges Construction of summaries useful for decision making High dimensional noisy information In many cases summary must be meaningful Construction of decision rules Variety of data sources In many cases decision rule must be meaningful Experimental designs Evaluation versus construction/refinement of decision rules.

Open Problems How should we design experiments if our goal is building or refining an adaptive treatment strategy? Present methods for estimating decision rules in an adaptive treatment strategy are biased if the feasible decision rules are constrained by interpretability.

Open Problems How might we use observational data to estimate good adaptive treatment strategies (e.g. decision rules)? How might we use data in which an adaptive treatment strategy was implemented to improve the decision rules?

This seminar can be found at: chicagobiostat0505.ppt Further information on adaptive treatment strategies can be found at:

Additional Issues in Managing the Chronic Disorder Treatment is often burdensome, especially over time Patient adherence is a critical issue Co-occurring problems are common

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?