Bayesian Statistics & Innovative Trial Design April 3, 2006 Jane Perlmutter

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

Common Goals: Efficient & Effective Drug Development Effective: Effective: Evidence based Evidence based Statistically sound Statistically sound Ethical Ethical Efficient: Efficient: As rapid as possible, without compromising science or safety As rapid as possible, without compromising science or safety As inexpensively as possible, with out compromising science or safety As inexpensively as possible, with out compromising science or safety

Assumptions Creative, innovative thinking about trial design can improve efficiency without compromising effectiveness 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 Innovative trial designs can have much leverage, because they can be applied to trials involving any disease or treatment

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

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

“Subjective” Component e.g. prior results, theoretical basis Inference “Data” Component i.e. current experiment Bayesian Approach

Opportunities Afforded by Bayesian Approaches Use Hierarchical Models to focus on optimal Use Hierarchical Models to focus on optimal Drugs Drugs Dosages Dosages Sub-groups Sub-groups Use Adaptive Designs to Use Adaptive Designs to Increase proportion of patients receiving best treatment Increase proportion of patients receiving best treatment Completing trial more rapidly with fewer patients Completing trial more rapidly with fewer patients

Challenges Raised by Bayesian Approaches Challenge Computationally intractable Computationally intractable Subjectivity associated with prior probabilities Subjectivity associated with prior probabilities Solution Use Monte Carlo simulation methods Use multiple scenarios and conduct sensitivity analyses or use uniform priors

Winkler, R.L. Why Bayesian Analysis Hasn’t Caught on in Healthcare Decision Making, International Journal of Technology Assessment in Health Care, 17:1, (2001). Strengths & Weaknesses

Barriers to Accepting Bayesian Approaches There is significant inertia and comfort with the status quo There is significant inertia and comfort with the status quo Most people are taught frequentist methods Most people are taught frequentist methods Limited resources are devoted to developing bio-statistical innovation Limited resources are devoted to developing bio-statistical innovation Journal editors and the FDA have been ambiguous about their acceptance of Bayesian approaches Journal editors and the FDA have been ambiguous about their acceptance of Bayesian approaches

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

If apparent treatment effect is true, groups will diverge & trial can be rapidly completed If apparent treatment effect is true, groups will diverge & trial can be rapidly completed If apparent treatment effect is random, groups will converge If apparent treatment effect is random, groups will converge Adaptive Designs Problems Problems Trials take too long and are too costly Trials take too long and are too costly Half of patients in trials do not receive optimal treatment Half of patients in trials do not receive optimal treatment Potential Solution Potential Solution Randomly & Equally Assign Patient Observe & Predict Responses Randomly & Unequally Assign Patients True Treatment Effect? yes no Adaptive Trial Design

Randomized Discontinuation Design Problem Problem Trials take too long and are too costly Trials take too long and are too costly Only a small subset of patients is likely to respond to new drugs Only a small subset of patients is likely to respond to new drugs Potential Solution Potential Solution Initially all patients receive experimental treatment Initially all patients receive experimental treatment Superiority is based on known responders only Superiority is based on known responders only Randomized Discontinuation Design All Patients Receive Experimental Treatment Respond? Yes Treatment Effect? Continue on Experimental Treatment Switch to Standard Treatment } 50%

“Out-of-the-Box” Clinical Trial Problems Problems Patient accrual is slow Patient accrual is slow <50% of eligible patients who are offered trials actually enroll <50% of eligible patients who are offered trials actually enroll Many patients are uncomfortable with random assignment Many patients are uncomfortable with random assignment Potential Solution Potential Solution If no disordinal interaction, fewer randomized patients are required to achieve same power 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 If there is a “patient- selection” main effect or interaction is found, they may prove interesting Out-of-the-Box Trial Design No Agree to be in Trial Selects own Treatment? Yes No

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

How Advocates Can Accelerate Innovation in Clinical Trial Design Become knowledgeable about sound alternative designs and inform other advocates Become knowledgeable about sound alternative designs and inform other advocates Ask researchers if they have considered more efficient designs Ask researchers if they have considered more efficient designs Advocate for more funding of statistical research and training Advocate for more funding of statistical research and training Critically assess potential FDA policy changes, and advocate for constructive change Critically assess potential FDA policy changes, and advocate for constructive change