Presentation on theme: "An Update on FDA’s Critical Path Initiative Statistical Contributions"— Presentation transcript:
1An Update on FDA’s Critical Path Initiative Statistical Contributions Robert T. O’Neill Ph.D.Director , Office of BiostatisticsCenter for Drug Evaluation and ResearchPresented at the 2005 FDA/Industry Statistics Workshop: September 14-16, 2005Marriott Wardman Park Hotel, Washington, DC
2The Critical Path Initiative Refers to the product development path from candidate selection to product launchCovers drugs, biologics, and medical devices – but today’s talk is mostly about drugs / biologicsInitiative was announced publicly by Dr. McClellan Tuesday, March 16, 2004
3What the “Critical Path” Is A serious attempt to bring attention & focus to the need for more scientific effort and publicly-available information on evaluative toolsEvaluative tools: The techniques & methodologies needed to evaluate the safety, efficacy & quality of pharmaceuticals as they move down the path
5Despite Advances in Science, Success Rate of Product Development has NOT Improved New compounds entering Phase I development today have 8% chance of reaching market, vs. 14% chance 15 years ago.Phase III failure rate now reported to be 50%, vs. 20% in Phase III, 10 years ago.
6Perceived Problem: The development process itself is becoming a serious bottleneck Current applied science and infrastructure date from last centuryFunding and progress in Development science has not kept pace with basic biomedical science.Science to evaluate safety and efficacy of potential new medical products, and enable manufacture, is different from basic discovery science.Need to fill gap in applied science-- to increase productivity and efficiency --to reduce cost of development process.
7Stakeholder Input: Overwhelming Support Overwhelming concurrence with:recognition of science infrastructure problemCP Initiative focus on research and collaboration,We heard this from: drug industry, patient groups, device companies and groups, biotech companies, others
8This is what we heard ! Demand Exceeds Supply Docket Demand for FDA Action Exceeds FDA Capacity: Far more proposed than FDA can undertake.Principles for setting priorities for FDA actions are on Science Board agenda.
9Overriding ConcernsClinical TrialsBiomarkers and Endpoints
10What is the problemPhase III trials are failing at a rate that is higher than expected - root causes ?What is the typical planning process for drug development / phase 3 trialsWhat can we change; what new tools can we use, and what can we do better in the future to improve Phase III success and efficiency of drug development
11Possible solutions / strategies Can statisticians help ? Are new study designs neededImpetus for Adaptive designs, two stage designs, enriched target population designsAre we planning correctly - Rethink how the study planning process occursIt’s the doseIt’s the scenario needing better planning - or analysis methodsBring consensus / closure to most pressing statistical issues at the core of decision makingGet involved in new emerging subject matter areas and impact them -genomics, proteonomics, nanotechnologyBroaden the multi-disciplinary roles, in industry, academia and regulatory bodies - internationally
12Our Proposal for the Critical Path Conduct Research , Gain Consensus, and Develop Guidance to Remove Obstacles to Efficient Drug Development and Enhance Success Rates of Clinical TrialsImprove the Processes and Approaches to Quantitative Analysis of Clinical Safety Data from Clinical Trials to Enhance Risk Assessment and Management InitiativesImprove the Statistical Understanding and Application of Modern Statistical Approaches to Product Testing and Process Control
13Clinical Trial Proposals for the Critical Path Missing data due to patient withdrawals and dropouts in clinical trialsFlexible / adaptive clinical trial designs to improve the information and success rate of trialsNon-inferiority active control studies when placebos can't be used - getting to consensus on appropriate methods for margin setting, data analysis and interpretation for various data rich and data poor scenariosDevelopment of consensus on the statistical handling of multiple endpoints in clinical trials.Clinical trial modeling and simulation as a tool for better design and interpretation of clinical trialsApplication of Bayesian Methods to Enhance the Success Rate of Clinical Trials
14Prioritize Efforts - Three separate yet related approaches Guidance DevelopmentMultiple endpointsNon-inferiorityTopics of high interestAdaptive / Flexible designsModeling / simulation / planning/Phase 2aOther Critical Path needs: safety , product quality
15Safety and Quantitative Risk Assessment Clinical Trials - Pre-Marketing Methods of applicationPlanning, data collection, statistical analysis planProcessNewly formed statistical safety team for more concentrated and focused adviceEarlier planning, modeling and simulation
16FDA Risk Management Guidances Life cycle of a drug Premarketing Risk Assessment (Premarketing Guidance)Development and Use of Risk Minimization Action Plans (RiskMAP Guidance)Good Pharmacovigilance Practices and Pharmacoepidemiology Assessment (Pharmacovigilance Guidance)
17Enhancing Product Quality Modern in process testing raises the possibility that alternatives to product quality should be consideredThere have also been advances in Process Analytical Technology (PAT) which depends on in process assessment of product quality all along the drug manufacturing process
18The Non -Inferiority Problem Current guidance is inadequate and the issues are poorly understood - must be fixedTerm introduced in ICH E9 ‘Statistical Principles for Clinical Trials’Some issues described in ICH E10 ‘Choice of Control Groups’A study design that provides an indirect measure of evidence of efficacy / safety
19What are the various objectives of the non-inferiority design To prove efficacy of test treatment by indirect inference from the active control treatmentTo establish a similarity of effect to a known very effective therapy - e.g. anti-infectivesTo infer that the test treatment would have been superior to an ‘imputed placebo’ ; ie. had a placebo group been included for comparison in the current trial. - a new and controversial area - choice of margin is the keyTo preserve a specified % effect of the AC
20How is the margin “ “ chosen based upon prior study data For a large treatment effect, it is easier - a clinical decision of how similar a response rate is needed to justify efficacy of a test treatment - e.g. anti-infectives is an example.For modest and variable effects, it is more difficult ; and some approaches suggest margin selection based upon several objectives.
21Complexities in choosing the margin (how much of the control treatment effect to give up) Margins can be chosen depending upon which of these questions is addressed:how much of the treatment effect of the comparator can be preserved in order to indirectly conclude the test treatment is effective - a clinical decision for very large effects; a statistical problem for small and modest effectshow much of a treatment effect would one require for the test treatment to be superior to placebo, had a placebo been used in the current active control study - a lesser standard than the above
22How convincing is the prior evidence of a treatment effect ? Do clinical trials of the comparator treatment consistently and reliably demonstrate a treatment effect - when they do not, what is the reason ?Study is too small to detect the effect - under powered for a modest effect sizeThe treatment effect is variable, and the estimate of the magnitude will vary from study to study, sometimes with NO effect in a given study - a BIG problem for active controlled studies (Sensitivity to drug effect)
23Importance of the assumption of constancy of the active control treatment effect derived from historical studiesIt is relevant to the design and sample size of the current study, to the choice of the margin, to the amount of bias built into the comparisons, to the amount of effect size one can preserve (both of these are likely confounded), and to the statistical uncertainty of the conclusion.Before one can decide on how much of the effect to preserve, one should estimate an effect size for which there is evidence of a consistent demonstration that effect size exists.
24Four approaches to the problem The simple case: specify a delta - not estimatedIndirect confidence interval comparisons (ICIC) (CBER/FDA type method, etc.)- thrombolytic agents in the treatment of acute MIVirtual method (Hasselblad & Kong, Fisher, etc.)- Clopidogrel, aspirin, placeboBayesian approach (Gould, Simon, etc.)- treatment of unstable angina and non-Q wave MI
25Current Guidance on Multiple Endpoints is inadequate Multiple primary endpoints Multiple secondary endpoints Composite endpoints Multiple composites Hierarchies Patient reported outcomes Decision Criteria for successA collaborative effort: PhRMA 2004 meeting on co-primary endpoints, manuscript
27New study designs Why a need for adaptive / flexible designs ? Enriching trials with patients having genomic profiles likely to respond or less likely to experience toxicityGoal of an adaptive / flexible designMid study changes that prospectively plan for modifications that preserve Type 1 errors and maximize chances for success
28Information adaptive designs / flexible designs Controversial Statististical Methodology is Available Why and where to use them?
29Why the need for adaptation? Design specifications often entail at least partial knowledge of the values of many planning (primary or nuisance) parameters that are unknown or at best might be guessed crudelySample size planning entails “educated” guess of effect size.Selection of a composite endpoint requires “educated” guess of where the potential effects lie and what noises may be.Others…..Hung
30Addressing a process issue: Scenario Planning: A Tool to Increase the Success Rate of Phase III trials and to Enhance Drug Development Planning Incorporates: Several linked linked study phases - continuum Multiple endpoints Missing data Use of all information in the process Safety Planning Modeling and simulation Flexible designs / development sequence / international
31What is Scenario Planning Modern approach to protocol planning and choice of clinical study designsUtilizing models for disease progression and endpoint selectionUtilizing simulation strategies for what if scenariosAssumes input from other studies and planning efforts - planned sequences of studies may matterAn aid for prospectively planning integrated analyses
32Disease Progression Modeling Endpoint selection and evaluationTrial Duration determinationFrequency and number of subject measurementsTradeoffs between clinical endpoints and patient reported outcomesEvaluate impact of missing data, informative treatment related censoringEvaluate multiplicity implications
33What would be observed if subjects had stayed in trial ? Impute values from subects staying in longerTestControlWhich path doyou choose ?BaselineHigher is bad12345Visit
34Disease Progression Models and Clinical Outcomes What model captures the functional relationship of the disease progression and the clinical outcome(s) to be used to measure treatment effectCan one function capture each of the clinical outcomes adequatelyIf not are several disease progression models used to express ‘response’
35Modern Protocol /Development Planning Sensitivity / Scenario planning Different statistical tools and strategiesChallenge and explore assumptionsMore multidisciplinary involvementIt is more than sample size planningStructured planning meetings that are different that current – formal Q & A’s not broad enoughLinks between phase planning and modeling efforts – currently too limited and stove piped
36Concluding remarks Meeting the Challenges of the Critical Path will require collaboration and resource allocationMultidisciplinary / collaborative planning and evaluation is needed now more than ever because issues becoming more complex - guidances can’t solve this - resources, exposure, experience, training willEfforts to move available appropriate statistical methods and concepts , possibly more complex, into the main stream by emphasis on understanding by the audience appropriate to the applicationGuidances don’t help here - need resources that can understand and communicateEfforts to maximize contributions of industry, academic and regulatory statisticians
37Concluding remark -Priority setting - Choosing the most pressing needs and the chances for success - currently being updatedThis is a national effort - not just FDA’s initiative - it will take a major coordinated effort to make progress