Statistical Considerations for Implementing the FDA CV

Guidance for T2DM

Craig Wilson, PhDNIC-ASA Fall Meeting

October 15, 2009

Regulatory History (1)

• Rosiglitazone (Avandia; GSK)– TZD for treatment of T2DM– Nissen and Wolski (May 2007)

• Meta-analysis of 42 studies (Ph2/3/4)– Increased risk of MI (OR=1.43; 95%CI [1.03, 1.98])– Possible increased risk of CV death (OR=1.64; 95%CI

[0.98, 2.74])

• Resulted in FDA black box warning

– Questions regarding validity of analysis• 6 of 48 studies excluded with no events

Regulatory History (2)

• FDA Advisory Committee Meeting (July 2008)– Need for data to assess CV risk in T2DM– Endpoints for consideration– Reasonable NI margin for risk ratio to rule out

excess CV risk

FDA CV Guidance for T2DM (1)

• Final guidance issued December 2008– Largely influenced by June 2008 advisory

committee meeting– Will be folded into draft FDA DM guidance

(February 2008) by end of 2009

• Key message: All development programs for T2DM should rule out “unacceptable increase in CV risk”

FDA CV Guidance for T2DM (2)

• Programs should analyze “important CV events”• Suggests CV mortality, MI, stroke (core MACE)• Suggests hospitalization for ACS, urgent

revascularization, “other endpoints” should be adjudicated– Only core MACE + hospitalization for unstable angina likely to

be accepted as primary endpoint per DIA meeting (September 2009)

• Offers guidance for new and existing development programs

• Investigational drugs must not increase CV risk by more than 80% vs. control to initially market; definitely rule out 30% increase to continue on market

New Studies/Programs• Prospective adjudication• High-risk population

– Advanced disease (eg, recent event or long duration of diabetes)

– Elderly– Renally impaired

• Meta-Analysis (Pooled Analysis)– Should include Ph2/3 studies– Comparison vs. control (placebo and/or active)– Long-term data (eg, ≥2 years)

• Stand-alone trial– Might also pool with other analyses

Existing Studies/Programs

• “Meta-analysis” of Ph2/3 data• “Unacceptable risk” margins discussed in

context of upper bound of 2-sided 95% CI for risk ratio vs. control– >1.8 Safety trial needed (stand-alone or

pool with Ph2/3 data) to market– Between 1.3 and 1.8 + favorable risk-benefit

market + post-market trial to rule out 1.3– <1.3 + favorable risk-benefit market; post-

market trial “generally may not be necessary”

Stand-Alone Trial Considerations

• Stand-alone trial: “…if the data from all the studies that are part of the meta-analysis will not by itself be able to show that the upper bound of the 2-sided 95% CI is <1.8, then an additional single, large safety trial should be conducted that alone, or added to other trials, would be able to satisfy this upper bound before NDA/BLA submission.”

• Stand-alone or pooled approach can also be used if between 1.3 and 1.8

• Guidance doesn’t address combining 1.8 and 1.3 analyses in same trial

• Guidance introduces possibility of approval for <1.8 when true risk ratio is 1.3

Advisory Committee Meetings

• Conducted in April 2009– SMQ MACE: CV-related events (serious or

non-serious) based on MedDRA SMQs– Custom MACE: CV-related events (serious or

non-serious) identified by FDA

Saxagliptin

• Onglyza; DPP-4 inhibitor; developed by BMS

• Presentation included controlled data up to 1 year

• Had favorable risk ratios– SMQ MACE (RR=0.85; 95% CI [0.52, 1.42])– Custom MACE (RR=0.2; 95% CI [0.04, 0.79])

• Approved by FDA in August 2009

Liraglutide

• Victoza; GLP-1; developed by Novo Nordisk• Presentation included controlled data up to 1

year• Had less favorable risk ratios

– SMQ MACE (RR=0.9; 95% CI [0.56, 1.31])– Custom MACE (RR=0.7; 95% CI [0.32, 1.57])

• Data comparing to placebo alone not as favorable

• Approved by EMEA in June 2009; still awaiting FDA approval

DIA Meeting

• Held September 2009• Core MACE (CV death, MI, stroke) only agreed primary

endpoint– Concerns regarding unstable angina adjudication (ability to

capture only serious/significant cases)– Some flexibility may exist for UA in the future

• Traditional statistical approaches encouraged (non-adaptive)– Concerns about Type 1 error protection

• Total of 18 CV study designs submitted to FDA so far• Suggestions that EMEA may follow a more “holistic”

approach to assessing CV risk than FDA

Possible Stand-Alone Trial Designs (1)

• Stand-alone trial most robust way to rule out CV risk– FDA prefers placebo comparison per

guidance

• Few opportunities to determine scope of possible analyses acceptable to FDA

• Must rely on reasonable available methods

Possible Stand-Alone Trial Designs (2)

• Time-to-event analysis (Cox regression) seemingly most appropriate analysis method– Reasonably simple and widely understood– Easily generates required hazard margin– Specific methods not stated in FDA guidance;

however, method should account for potential non-constant hazard

– Intent to treat• Survival function specification may rely on

completed trial– Proportional hazards assumption?– Implications of interim analyses?

Possible Stand-Alone Trial Designs (3)

• Traditional analysis– Single analysis after 611 events to rule out

1.3– Pros: Widely accepted; no concern about

Type 1 error control– Cons: One-and-done scenario; time

consideration if needed to market

Possible Stand-Alone Trial Designs (4)

• Separate trials (Fleming; DIA)– Conduct one trial to rule out 1.8 (122 events)– Conduct a separate trial (611 events) to rule

out 1.3– Pros: Widely accepted; no concern about

Type 1 error control– Cons: Still one-and-done scenarios; what if

margin barely missed? What if trials give conflicting results? Cost considerations

Possible Stand-Alone Trial Designs (5)

• Traditional analysis with single step-down interim look– Analyze after 122 events to rule out 1.8– If successful, continue to 611 events to rule

out 1.3; otherwise, declare futility and stop the trial

– Pros: Efficient use of study population– Cons: Prevents assessment of 1.3 if 1.8 can’t

be ruled out; power reduction to rule out 1.3 without more events

Possible Stand-Alone Trial Designs (6)

• Adaptive monitoring (Connor and Berry; January 2009)– Currently being used for CV trial for Libigel (BioSante) to rule out

an upper bound of 2.0– Could be modified to current CV guidance– Use predictive probabilities to monitor accrual and likelihood of

trial success (<1.3)– Conduct periodic pre-planned analyses

• Stop accrual for high predictive probability of trial success• Declare futility for low predictive probability of trial success• Stop trial for success when upper bound of CI is <1.3

– Type 1 error and power confirmed via simulation– Pros: Allows frequent monitoring of trial; potentially reduces

total subjects enrolled; may allow interim monitoring to rule out 1.8

– Cons: Potential for regulatory agencies to be skeptical, given strong interest in Type 1 error protection (DIA meeting)

Possible Stand-Alone Trial Designs (7)

• Group sequential design (Fleming; DIA)– Analyze after 122 events to rule out 1.8– If successful, continue to 611 events to rule out 1.3;

use alpha-spending function (OBF) to control Type 1 error for 2 analyses

– Pros: Efficient use of study population; still allows assessment of 1.3 even if 1.8 can’t be ruled out

– Cons: Would potentially require more time to market (if 1.8 can’t be ruled out); difficult to rule out 1.8 at interim unless true hazard highly favorable; may require more events than stated to maintain desired power

Possible Stand-Alone Trial Designs (8)

• Group sequential design with step-down– Use alpha-spending function (OBF) and repeated CIs

to rule out 1.8 (Durrleman and Simon [1990])– If successful, use separate alpha-spending function

(OBF) and repeated CIs to rule out 1.3; otherwise, stop the trial for futility

– Pros: Efficient use of study population; provides more power to rule out 1.8 if true hazard is close to 1.0; provides early assessment to rule out 1.8

– Cons: Prevents assessment of 1.3 if 1.8 can’t be ruled out; potential power reduction to rule out 1.3 without adding more events

Superiority?

• Could design from beginning as superiority trial using traditional approach– Would likely require substantial data– Unlikely to get label claim

• Step-down analysis once 1.3 ruled out?– Dependent on proximity of upper 95% CI

bound to 1.3– Adaptive design likely best suited for task– How long do you look?

Questions?