Loading in 5 sec....

Bayesian Statistics & Innovative Trial DesignPowerPoint Presentation

Bayesian Statistics & Innovative Trial Design

- 68 Views
- Uploaded on
- Presentation posted in: General

Bayesian Statistics & Innovative Trial Design

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Bayesian Statistics & Innovative Trial Design

April 3, 2006

Jane Perlmutter

janep@gemini-grp.com

- Introduction
- Bayesian vs. Frequentists Statistics
- Some Innovative Designs
- Adaptive Designs
- Random Discontinuation Designs
- “Out of the Box” Designs

- Conclusions

- 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

- 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

- Introduction
- Bayesian vs. Frequentists Statistics
- Some Innovative Designs
- Adaptive Designs
- Random Discontinuation Designs
- “Out of the Box” Designs

- Conclusions

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

“Subjective”

Component

e.g. prior results,

theoretical basis

“Data”

Component

i.e. current experiment

Inference

- 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

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

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).

- 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

- Introduction
- Bayesian vs. Frequentists Statistics
- Some Innovative Designs
- Adaptive Designs
- Random Discontinuation Designs
- “Out of the Box” Designs

- Conclusions

True Treatment Effect?

Randomly & Equally Assign Patient

Observe & Predict Responses

Randomly & Unequally Assign Patients

yes

no

- 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

}

Continue on

Experimental

Treatment

50%

Treatment

Effect?

Yes

Switch to

Standard

Treatment

50%

All Patients Receive

Experimental

Treatment

Respond?

- 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

No

No

Agree to be

in Trial

Selects own

Treatment?

Yes

- 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

- Introduction
- Bayesian vs. Frequentists Statistics
- Some Innovative Designs
- Adaptive Designs
- Random Discontinuation Designs
- “Out of the Box” Designs

- Conclusions

- 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