Bayesian statistics innovative trial design
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Bayesian Statistics & Innovative Trial Design. April 3, 2006 Jane Perlmutter [email protected] Topics. Introduction Bayesian vs. Frequentists Statistics Some Innovative Designs Adaptive Designs Random Discontinuation Designs “Out of the Box” Designs Conclusions. .

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Bayesian statistics innovative trial design

Bayesian Statistics & Innovative Trial Design

April 3, 2006

Jane Perlmutter

[email protected]


Topics

Topics

  • Introduction

  • Bayesian vs. Frequentists Statistics

  • Some Innovative Designs

    • Adaptive Designs

    • Random Discontinuation Designs

    • “Out of the Box” Designs

  • Conclusions


Common goals efficient effective drug development

Common Goals: Efficient & Effective Drug Development

  • Effective:

    • Evidence based

    • Statistically sound

    • Ethical

  • Efficient:

    • As rapid as possible, without compromising science or safety

    • As inexpensively as possible, with out compromising science or safety


Assumptions

Assumptions

  • Creative, innovative thinking about trial design can improve efficiency without compromising effectiveness

  • Innovative trial designs can have much leverage, because they can be applied to trials involving any disease or treatment


Topics1

Topics

  • Introduction

  • Bayesian vs. Frequentists Statistics

  • Some Innovative Designs

    • Adaptive Designs

    • Random Discontinuation Designs

    • “Out of the Box” Designs

  • Conclusions


Frequentist vs bayesian methods

Frequentist vs Bayesian Methods

Spiegelhalter, D. J. et.al. An Introduction to Bayesian Methods in Health Technology Assessment, BMJ, 319, 508-511 (1999).


Bayesian approach

Bayesian Approach

“Subjective”

Component

e.g. prior results,

theoretical basis

“Data”

Component

i.e. current experiment

Inference


Opportunities afforded by bayesian approaches

Opportunities Afforded by Bayesian Approaches

  • Use Hierarchical Models to focus on optimal

    • Drugs

    • Dosages

    • Sub-groups

  • Use Adaptive Designs to

    • Increase proportion of patients receiving best treatment

    • Completing trial more rapidly with fewer patients


Challenges raised by bayesian approaches

Challenge

Computationally intractable

Subjectivity associated with prior probabilities

Solution

Use Monte Carlo simulation methods

Use multiple scenarios and conduct sensitivity analyses or use uniform priors

Challenges Raised by Bayesian Approaches


Bayesian statistics innovative trial design

Strengths & Weaknesses

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).


Barriers to accepting bayesian approaches

Barriers to Accepting Bayesian Approaches

  • There is significant inertia and comfort with the status quo

  • Most people are taught frequentist methods

  • Limited resources are devoted to developing bio-statistical innovation

  • Journal editors and the FDA have been ambiguous about their acceptance of Bayesian approaches


Topics2

Topics

  • Introduction

  • Bayesian vs. Frequentists Statistics

  • Some Innovative Designs

    • Adaptive Designs

    • Random Discontinuation Designs

    • “Out of the Box” Designs

  • Conclusions


Adaptive designs

True Treatment Effect?

Randomly & Equally Assign Patient

Observe & Predict Responses

Randomly & Unequally Assign Patients

yes

no

Adaptive Designs

  • Problems

    • Trials take too long and are too costly

    • Half of patients in trials do not receive optimal treatment

  • Potential Solution

Adaptive Trial Design

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

  • If apparent treatment effect is random, groups will converge


Randomized discontinuation design

}

Continue on

Experimental

Treatment

50%

Treatment

Effect?

Yes

Switch to

Standard

Treatment

50%

All Patients Receive

Experimental

Treatment

Respond?

Randomized Discontinuation Design

  • Problem

    • Trials take too long and are too costly

    • Only a small subset of patients is likely to respond to new drugs

  • Potential Solution

Randomized Discontinuation Design

  • Initially all patients receive experimental treatment

  • Superiority is based on known responders only


Out of the box clinical trial

No

No

Agree to be

in Trial

Selects own

Treatment?

Yes

“Out-of-the-Box” Clinical Trial

  • Problems

    • Patient accrual is slow

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

    • Many patients are uncomfortable with random assignment

  • Potential Solution

  • If no disordinal interaction, fewer randomized patients are required to achieve same power

  • If there is a “patient-selection” main effect or interaction is found, they may prove interesting

Out-of-the-Box Trial Design


Topics3

Topics

  • Introduction

  • Bayesian vs. Frequentists Statistics

  • Some Innovative Designs

    • Adaptive Designs

    • Random Discontinuation Designs

    • “Out of the Box” Designs

  • Conclusions


How advocates can accelerate innovation in clinical trial design

How Advocates Can Accelerate Innovation in Clinical Trial Design

  • Become knowledgeable about sound alternative designs and inform other advocates

  • Ask researchers if they have considered more efficient designs

  • Advocate for more funding of statistical research and training

  • Critically assess potential FDA policy changes, and advocate for constructive change


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