American Society of Clinical Oncology

Cancer Leadership Council

Cancer Research and Prevention Foundation

Coalition of Cancer Cooperative Groups

Oncology Nursing Society

A Response to the Crisis in

Clinical Trials:

Innovative Trial Design

November 7, 2006

Jane Perlmutter, Ph.D.

janep@gemini-grp.com

Four Threats Slow Patient Accrual

Reduced Funding

Legislation Challenge (U.S. Senate Bill S.1056)

Tier I approval after Phase I testing

Primarily based on clinical evaluation, not statistical

Analysis

Judicial Challenge (Abigail Alliance et. al. vs.

FDA)

Constitutional right to assume the risk of taking

unproven experimental drugs

Key Points Innovations in trial design can ameliorate the

“four threats” by: Making them more attractive to patients Reducing the number of patients and funding

requirements Speeding conclusions

Innovative trial designs can rigorously adhere to the principles of evidence-based medicine

Innovations in trial design have high leverage, because they can be applied to any disease or treatment

Paradigm Shift

OOlldd NNeeww

PPaarraaddiiggmm Hypothesis Testing Learning

QQuueessttiioonn

How likely are the trial results, given there really is no difference among treatments?

How likely is there a true difference among treatments, given the trial data?

DDrruugg AApppprroovvaall Pivotal Trial Weight of Evidence

TTrriiaall DDeessiiggnnss Single Stage Adaptive

SSttaattiissttiiccss Traditional Bayesian

Thomas Bayes

1702 -- 1761

Trial Data

Prior Beliefs

Updated Beliefs

Bayesian Framework

The Paradigm Shift: From hypothesis testing to updating or revising beliefs in light of new evidence

The Advantages: Formal system for incorporating existing information

Natural approach to inference

Generally more efficient

Well suited for decision making

The Challenges: Determining appropriate prior probabilities

Computational complexity

Lack of familiarity

Lack of software tools

Adaptive Designs

Multi-stage Designs: later stages based on interim results

Example Adaptation Rules:

Allocation Rule: How are patients allocated to treatment arms? (Note: patients are always randomly

assigned, but the relative frequency may be changed, including adding or dropping arms)

Sampling Rule: How many subjects should be sampled at the next stage? (Note: this may change due to surprises about accrual rate, sample variance, etc.)

Stopping Rule: When should the study be terminated due to observed efficacy, harm, futility, or safety

If apparent treatment effect is true, groups will diverge & trial can be rapidly completed

If apparent treatment effect is random, groups will converge

Patient Allocation Adaptive Design

Difficulties with Traditional Approaches Trials require many patients, take too long, and are too costly

Half of patients in the trial do not receive optimal treatment

Potential Solution

Patient Allocation Adaptive Design

Randomly &

Equally Assign

Patient

Observe &

Predict

Responses

Randomly &

Unequally Assign

Patients

True

Treatment

Effect?yes

no

Response Rate

Call to Action to Accelerate the

Paradigm Shift

Become knowledgeable about adaptive designs & Bayesian statistics

Ask researchers if they have considered using adaptive designs

Advocate for more funding of statistical research, education, and software tools

Work with the FDA to accelerate release of their recently announced guidance documents on Bayesian statistics and Adaptive Trials

Potential Summit Actions

Form an Innovative Design Working Group

Invite statistical thought leaders to future

summits

Increase the priority of this issue on the agenda

of our conveners

Other?

American Society of Clinical Oncology

Cancer Leadership Council

Cancer Research and Prevention Foundation

Coalition of Cancer Cooperative Groups

Oncology Nursing Society

Back-Up Material

Example Innovative Multi-stage Designs

Randomized Discontinuation Design

Patient Preference Design

References

The Four Threats

Adaptive Designs

Bayesian Statistics

Randomized Discontinuation

Design Difficulties with Traditional Approaches

Trials take too long and are too costly

Only a small subset of patients typically respond to new drugs

Potential SolutionRandomized Discontinuation Design

Initially all patients receive the experimental treatment

Superiority is based on patients who are initially stabilized with the experimental treatment

All Patients

Receive

Experimental

Treatment

Treatment

Effect?}50%

50%

100%

100%

Continue Ex.

Treatment

Continue Ex.

Treatment

Switch to Std.

Treatment

Switch to Std.

Treatment

Change in Tumor Size

Freidlin B, Simon R. Evaluation of Randomized Discontinuation Design. Journal of Clinical Oncology. 23:22; 5094-5098; 2005.

Patient Preference Design(“Out of the Box” Design)

Difficulties with Traditional Approaches Patient accrual is slow

<50% of eligible patients who are offered trials actually enroll

Some patients are uncomfortable with random assignment

Potential Solution If the direction of any

treatment effect is independent of whether or not patients are randomized, fewer randomized patients are required to achieve same power

Any “patient-selection” findings may themselves prove interesting

Patient Preference Design

Torgerson D, Sibbald B, Understanding controlled trials: What is a

patient preference trial? British Medical Journal. 316:360; 1998.

No

Agree to be

in Trial

Selects own

Treatment?

Experimental

Treatment Standard Treatment

Randomized Treatment

Patient Selected Treatment

Yes

No

Recommended Reading

The Four Threats http://www.cms.hhs.gov/NationalHealthExpendData http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.ht

ml News. Journal of the National Cancer Institute. 98:18;

1268-1270; 2006. Position Paper. Society of Clinical Trials. 3; 154-157; 2006.

Adaptive Trials Gallo P, Chuang-Stein C, Dragalin V, Gaydos B, Krams M,

Pinheiro J. Adaptive Designs in Clinical Drug Development---An Executive Summary of the PhRMA Working Group. Journal of Biopharmaceutical Statistics. 16: 275–283; 2006.

Recommended Reading

Bayesian Statistics Berry DA (2006). Bayesian clinical trials. Nature Reviews:

Drug Discovery. 5: 27-36; 2006. Goodman, S.N. Toward Evidence-Based Medical Statistics:

The P Value Fallacy. Annals of Internal Medicine. 130:995-1021; 1999.

Spiegelhalter DJ, Keith R, Abrams KR, Myles JP. Bayesian Approaches to Clinical Trials and Health-Care Evaluation. John Wiley & Sons, Ltd. 2004.

Winkler, R.L. Why Bayesian Analysis Hasn’t Caught on in Healthcare Decision Making. International Journal of Technology Assessment in Health Care. 17:1, 56-66; 2001.