Statistical Approaches to Support Device Innovation- FDA View Lilly Yue, Ph.D. Deputy Director, Division of Biostatistics CDRH/FDA CRT 2012, Washington, DC No Conflict of Interest

Today’s Topics Bayesian Methods Adaptive Designs Propensity Score Methods

Bayesian Methods Allow for use of prior information if available, e.g., patient-level data from previous studies. Previous studies in the U.S. Studies conducted overseas (if thought exchangeable) Possibly registry information Allow for flexible study design and analysis. Predictive probabilities are used not only to stop early in a planned fashion for success or futility but also sometimes to stop recruiting based on a predictive model that is estimated during the trial.

Bayesian Methods Simulate frequentist properties of Bayesian designs. These approaches are highly labor intensive for sponsors and for FDA reviewers. “Guidance for the Use of Bayesian Statistics in Medical Device Trials” released in final form in February, 2010. http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm071072.htm

Today’s Topics Bayesian Methods Adaptive designs Propensity Score Methods

Adaptive Designs An adaptive design - Uses accumulated data to decide on how to modify aspects of the trial, without undermining the validity and integrity of the trial. Examples: Group sequential design, Bayesian design and sample size re-estimation approach FDA “Draft Guidance on Adaptive Design Clinical Trials for Drugs and Biologics”, February, 2010. Adaptive designs for non-randomized comparative studies (with concurrent or historical control) or single-arm studies?

Adaptive Design Principle An adaptive design should be adaptive by “design” not an ad hoc change of the trial conduct and analysis. Adaptation is a prospective design feature, not a remedy for poor planning. A mid-course design change through an amendment may be inappropriate. In protocol, pre-specify all rules for adaptive changes and for final analysis; Trial design simulations should be submitted to FDA along with the protocol. Implementing an adaptive design is a challenge.

Adaptive Design Example Superiority testing w.r.t. treatment difference in means, Delta = New – Con. Delta : [2, 4], clinically plausible Fixed sample design: Delta 2 3 4 Sample Size 1051 467 263 A group sequential approach -High up-front commitment Design for delta=2 with 2 interim looks, but quit early if delta =4 Initial sample size =1096 If delta=4, exit after 365 patients with 88% probability by simulation

Adaptive Design Example (cont.) A sample size re-estimation - Low up-front commitment: Design for delta =4, but extend sample size if necessary at an interim look Initial sample size = 267; interim look after 200 patients If an estimate of delta is small, extend the trial beyond 267 patients Minimum clinically acceptable treatment difference needs to be pre-specified in protocol and agreed upon by sponsor and FDA.

Logistical and Operational Issues Who will perform the interim analysis? How will the sponsor ensure that the interim results are not widely known? Who will have the authority to decide what type of design change is needed? Should there be any sponsor involvement in the adaptive decision? How much involvement? Logistical rather than statistical problems appear to be the major obstacle to adopting flexible adaptive clinical trials.

Regulatory Considerations Early interaction with the FDA is essential! Adaptive design details need to be clearly pre-specified in protocol; simulation information and computer code need to be submitted to FDA for IDE evaluation.

Today’s Topics Bayesian Methods Adaptive designs Propensity Score Methods

Propensity Score Methods Non-randomized (observational) studies could play a substantial role in the pre-market and post-market evaluation of medical device. When indeed appropriate, pre-market comparative observational studies may be conducted for safety and/or effectiveness with concurrent or historical control. However, limitations with observational studies could compromise the objectivity of resulting causal inference. Propensity score methods may be able to help.

Use of Propensity Score Methods Design Phase Help create distribution balance of covariates between the treatment groups. Help specify statistical analysis plan (SAP) for treatment comparison on outcome data. Without access to any outcome data! Analysis phase Treatment comparison on outcome data, within propensity score subclasses or based on matched pairs.

Objectively Design Observational Studies to Approximate RCT Help create distribution balance of covariates between the treatment groups - Q: At what time and by which entity will these be performed? When to submit to the FDA for evaluation? Help specify statistical analysis plan (SAP) for treatment comparison on outcome data - Q: When to submit to the FDA for evaluation? Without access to any outcome data! - Q: Is there any masking mechanism established? All of above needs to be planned in advance and pre-specified in protocol. Be transparent!

Concluding Remarks The statistical approaches discussed above could play an important role in the cardiovascular device innovation in the USA. The correct use of the statistical tools is essential.