Overview of Adaptive Treatment Regimes Sachiko Miyahara Dr. Abdus Wahed

Overview of Adaptive Treatment Regimes

Sachiko MiyaharaDr. Abdus Wahed

Before starting the presentation…

≠Adaptive

Treatment Regimes

Adaptive Experimental

Design

Adaptive Treatment Regimes vs. Adaptive Experimental Design

• Adaptive Treatment Regimes“…adaptive as used here refers to a time- varying therapy for managing a chronic illness” (Murphy,2005)

• Adaptive Experimental Design“…such as designs in which treatment allocation probabilities for the present patients depend on the responses of past patients” (Murphy,2005)

Outline1. What is Adaptive Treatment Regime?

-Definition-Example-Objective

2. How to decide the best regime?- 3 different study designs- Comparison of 3 designs

3. Trial Example (STAR*D)

4. Inference on Adaptive Treatment Regimes

What is Adaptive Treatment Regime?

Definition:a set of rule which select the best

treatment option, which are made based on subjects’ condition up to

that point.

What is Adaptive Treatment Regime?

A

A’

B1

B1’

B2

B2’

B2’

B2

Non Responder

Responder

Responder

Non Responder

Patient

B1

B1’

8 Possible Policies

(1) Trt A followed by B1 if response, else B2 (AB1B2)

(2) Trt A followed by B1 if response, else B2’ (AB1B2’)

(3) Trt A followed by B1’ if response, else B2 (AB1’B2)

(4) Trt A followed by B1’ if response, else B2’ (AB1’B2’)

(5) Trt A’ followed by B1 if response, else B2 (A’B1B2)

(6) Trt A’ followed by B1 if response, else B2’ (A’B1B2’)

(7) Trt A’ followed by B1’ if response, else B2 (A’B1’B2)

(8) Trt A’ followed by B1’ if response, else B2’ (A’B1’B2’)

What is the objective of the Adaptive Treatment Regimes?

• Objective:To know which treatment strategy works the best, given a patient’s history.

A treatment naïve patient comes to a physician’s office.

Questions:1. What treatment strategy should

the physician follow for that patient? 2. How should it be decided?

What is the objective of the Adaptive Treatment Regime?

If one knew…(T be the outcome measurement) 1. E(T| AB1B2) = 15

2. E(T| AB1B2’) = 14

3. E(T| AB1’B2) = 18

4. E(T| AB1’B2’) = 17

5. E(T| A’B1B2) = 20

6. E(T| A’B1B2’) = 19

7. E(T| A’B1’B2) = 13

8. E(T| A’B1’B2’) = 12

Best Regime for the patient

In Reality…

Problems:

1. E(T| . ) are not known (need to estimate)2. How can one accurately and efficiently estimate E(T| . )?

How to estimate the expected outcome?

Three study designs:

1. A clinical trial with 8 treatments

2. Combine existing trials

3. SMART (Sequential Multiple Assignment Randomized Trials)

Design 1: A clinical trial with 8 Treatment Policies

AB1B2

AB1B2’

AB1’B2

AB1’B2’Sample

= Randomization

A’B1B2

A’B1B2’

A’B1’B2

A’B1’B2’

Design 2: Combining Existing Trials

A

A’

B1

B1’

+

Trial 1 Trial 5Trial 3Trial 2 Trial 4

+ + +

Responder to A only

Responder to A’ only

Non Responder to A only

Non Responder to A’ only

B1

B1’

B2

B2’

B2

B2’

Sequential Multiple Assignment Randomized Trials (SMART) proposed by Dr. Murphy

The SMART designs were adapted to:- Cancer (Thall 2000)- CATIE (Schneider 2001) – Alzheimer's Disease- STAR*D (Rush 2003) – Depression

Design 3: SMART

Design 3: SMART

A

A’

B1

B1’

B2

B2’

B2’

B2

B1’

B1

Non Responder

Responder

Responder

Non Responder

Sample

= Randomization

Comparisons of 3 Study Designs

Question:A Trial with 8 Trts

Combined Trial

SMART

1. Does it serve the purpose of finding the best strategy?

2. Is it feasible?

3.Can we assess the trt effects using a standard statistical method?

Yes Yes

Yes

Yes

Maybe

No

Maybe

No

No

Sequenced Treatment Alternatives To Relieve Depression (STAR*D)

1.What is STAR*D? 2.The Study Design

What is STAR*D?

• Multi-center clinical trial for depression

• Largest and longest study to evaluate depression

• N=4,041

• 7 years study period

• Age between 18-75

• Referred by their doctors

• 4 stages (3 randomizations)

STAR*D Study Design: Level 1

Non Responder

Responder

Eligible Subjects

CIT

CIT

Go to Level 2


CIT+CT

BUP

CT

VEN

SERSwitch

Add on

Lev 1 NonResponder

= Subject’s Choice

= Randomization

CIT+BUS

CIT+BUP


Lev3 Med+Li

Lev3 Med+Li

NTP

MIRTSwitch

Add on

Lev 2 NonResponder

= Subject’s Choice

= Randomization


VEN+MIRT

TCP

Lev 3 NonResponder

= Randomization

Details on Inference from SMART

• Remember the goal is to estimate E(T|AB1B2)

• First, how can we construct an unbiased estimator for

E(T|AB1B2)?


• Let us ask ourselves, what would we have done if everyone in the sample were treated according to the strategy AB1B2 ?

AB1

B2Non

Responder

Responder

Patient

• What would we have done if everyone in the sample were treated according to the strategy AB1B2 ?

Answer: E(T|AB1B2) = ΣTi/n


Applies to 8-arm randomization trial

• But in SMART, we have not treated everyone with AB1B2


A

B1

B1’

B2

B2’Non

Responder

Responder

Sample

• Let C(AB1B2) be the set of patients who are treated according to the policy AB1B2


A

B1

B1’

B2

B2’Non

Responder

Responder

Sample

• We defineR = Response indicator (1/0)

Z1 = Treatment B1 indicator (1/0)

Z2 = Treatment B2 indicator (1/0)

Then C(AB1B2) = {i: [RiZ1i + (1-Ri)Z2i]=1}


Details on Inference from SMARTOne would define

E(T|AB1B2) = Σ[RiZ1i + (1-Ri)Z2i]Ti/n’

Where n’ is the number of patients in C(AB1B2).

This estimator would be biased as it ignores the second randomization.

• There are two types of patients in the set C(AB1B2) who were treated according to the policy AB1B2

A responder who received B1

andA nonresponder who received B2



A

B1

B1’

B2

B2’

Non Responder

Responder

Sample

• Assuming equal randomization,A responder who received B1 was equally eligible to receive B1

’

A responder who received B2 was equally eligible to receive B2

’


• ThusA responder who received B1 in C(AB1B2) is representative of another patient who received B1

’

andA non-responder who received B2 in C(AB1B2) is representative of another patient who received B2

’


• We define weights as follows A responder who received B1 in C(AB1B2) receives a weight of 2 [1/(1/2)], also

A non-responder who received B2 in C(AB1B2) receives a weight of 2 [1/(1/2)]

While everyone else receives a weight of zero.


Details on Inference from SMARTUnbiased estimator

E(T|AB1B2) = Σ[RiZ1i + (1-Ri)Z2i]Ti/(n/2)

And, in general,

E(T|AB1B2) = Σ[RiZ1i /π1+ (1-Ri)Z2i /π2]Ti/n

This estimator is unbiased under certain assumptions

Issues

• Compare treatment strategies • Wald test possible but needs to derive

covariance between estimators (which may not be independent of each other)

• In survival analysis setting, how to derive formal tests to compare survival curves under different strategies

• Is log-rank test applicable?• Can the proportional hazard model be

applied here?

Issues

• Efficiency issues• How can one improve efficiency of the

proposed estimator• How to handle missing data (missing

response information, censoring, etc.)• How to adjust for covariates when

comparing treatment strategies• And most importantly,

Issues

• Is it possible to tailor the best treatment strategy decisions to individual characteristics?• For instance, could we one day hand

over an algorithm to a nurse (not physician) which would provide decisions like “If the patient is a caucacian female, age 50 or over, have normal HGB levels, bla bla bla…the best strategy for maintaining her chronic disease would be……..”

ATSRG link

http://www.pitt.edu/~wahed/ATSRG/main.htm

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Overview of Adaptive Treatment Regimes Sachiko Miyahara Dr. Abdus Wahed