Methods for Estimating the Decision Rules in Dynamic Treatment Regimes S.A. Murphy Univ. of Michigan IBC/ASC: July, 2004

Dynamic Treatment Regimes

Dynamic Treatment Regimes are individually tailored treatments, with treatment type and dosage changing with ongoing subject information. 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

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

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 Thall et al. (2000) Treatment of Prostate Cancer

k Decisions Observations made prior to j th decision Action at j th decision Primary Outcome: for a known function f

A dynamic treatment regime is a vector of decision rules, one per decision If the regime is implemented then

Methods for Estimating Decision Rules

Three Methods for Estimating Decision Rules Q-Learning (Watkins, 1989) ---regression A-Learning (Murphy, Robins, 2003) ---regression on a mean zero space. Weighting (Murphy, van der Laan & Robins, 2002) ---weighted mean

One decision only! Data: is randomized with probability

Goal Choose to maximize:

Q-Learning Minimize

A-Learning Minimize

Weighting

Discussion

Consistency of Parameterization ---problems for Q-Learning Model Space ---bias ---variance

Q-Learning Minimize

Discussion Consistency of Parameterization ---problems for Q-Learning Model Space ---bias ---variance

Points to keep in mind The sequential multiple assignment randomized trial is a trial for developing powerful dynamic treatment regimes; it is not a confirmatory trial. Focus on MSE recognizing that due to the high dimensionality of X, the model parameterization is likely incorrect.

Goal Given a restricted set of functional forms for the decision rules, say, find

Discussion Mismatch in Goals ---problems for Q-Learning & A-Learning

Suppose our sample is infinite. Then in general neither or is close to

Open Problems How might we “guide” Q-Learning or A-Learning so as to more closely achieve our goal? Dealing with high dimensional X-- feature extraction- --feature selection.

This seminar can be found at: ibc_asc_0704.ppt My address: