Bayesian Statistics & Innovative Trial Design

Bayesian Statistics & Innovative Trial Design April 3, 2006 Jane Perlmutter janep@gemini-grp.com

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 • 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 • 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

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

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

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

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 • 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

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

} 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

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

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

Bayesian Statistics & Innovative Trial Design