Moving from US FDA focus to Global focus – Importance of Standards Margaret Minkwitz Sept 16, 2010

Definitions of terms used Health authorities – agencies responsible for review and approval of new medicines (US Food & Drug Administration (FDA) Common Technical Document – Internationally agreed structure and content details for a submission to the health authorities Standards: agreed format and data structure details Includes meaning of the variables Details in agreed structure Globally agreed and maintained – updated and reviewed Version controlled

Do we have a common understanding of the issues? Are we talking the same language?

Health Authorities: reasons to standardize data collection and reporting Ability to evaluate data across products / companies Evaluation of common effects across treatments (class effects) Ability to use common tools to verify the reported results Primarily the FDA Other health authorities starting to follow suit Ability to compare across data presented on public web sites – common terms and reporting structure Ability to standardize the product labels so that doctors can compare the properties of treatments when deciding which treatment to prescribe to a patient

Additional reasons for companies to embrace standards Co-development – 2 companies sharing the development costs and risks (example: AstraZeneca and BMS – ONGLYZA ® for diabetes, TV ad notes both companies) Outsource partnering with a Contract Research Organization (CRO) for study conduct and reporting CRO partner doing studies for Pharmaceutical Company From protocol to report (full service) Strategic (portion of the study outsourced – analysis programs) In-licensing product from development at another company Work with some studies in original company sources Need to integrate that data with new studies done

International Conference on Harmonization Health Authorities agreed to accept the Common Technical Document (CTD) for submissions for market authorization European Medicines Agency US Food and Drug Administration Japanese Health Authority Canadian Health Authority Industry guidance available for design, analysis, and reporting clinical studies FDA has specific guidance documents ICH guidance documents Other health Authorities have guidance documents

In preparing for Product Market Authorization (Submission of CTD) Need to ensure that all relevant guidance documents are reviewed Determine the studies needed to provide data required for agreed key markets (1 st countries to receive package, CTD) Plan study designs with details around Error control Approaches to handling missing data Approaches to handling multiple comparisons Appropriate analysis models given the primary variable for study Location of studies (multi country study – evaluate issues) Statistical power and sample size Plan for data collection and reporting (standards)

Tools to standardize data collection and reporting MedDRA – Medical Dictionary for Regulatory Activities Adverse event reporting Medical terminology reporting (Medical history) CDISC – Clinical Data Interchange Standards Consortium SDTM – Standard Data Tabulation Model ADaM – Analysis Data Model eCTD – Electronic Common Technical Document Clinical Trials web site reporting requirements (FDAAA) Web sites Reporting clinical studies being run Reporting data from completed clinical studies (within 1 year)

MedDRA structure Adverse event reported by patient Reported Rapid heart rate Hierarchical structure (text and code) – coded to common terminology System Organ ClassCardiac High level termRhythm abnormality Preferred termTachycardia If instead of rapid heart rate, a heart rate value was reported Reported/Preferred termHeart rate120 beats/minute System Organ Class Investigations High level termVital sign

Features of SDTM Domain – aggregation of specific type of information DE - Demography – age, sex, race, ethnicity, country, etc. VS - Vital signs – pulse, diastolic blood pressure, systolic blood pressure, respiration rate, temperature, etc. Variable – information includes VS test nameDIABP VS test descriptionDiastolic Blood Pressure VS test unitsmmHg VS test result74 Variable extensions: position code listsupine, sitting, standing, not specified method code list manual, automatic, etc.

Features of ADaM Similar to SDTM but the data are statistical analysis an parameters Analysis methodAnalysis of Variance ComparisonA vs. B estimate (trt diff) Statistical testt-test Test value5.66 Prob > |t|< Parameter namemean Parameter estimate5.61 Parameter variability nameStandard Error Parameter variability estimate0.99

eCTD Standards Common structure – Table of Content Clinical Section includes Section 2: Clinical Overview; Clinical Summary of Pharmacology. Efficacy, Safety, Benefit/Risk Section 5: Study reports for key studies; detailed supporting tables (Integrated summary of safety and efficacy) Regulatory Section: Section 1: Specifics around the particular health authority submission Information on communications and interactions during the program Details specific to local requirements Delivered as an electronic CTD – electron transfer

Web site expectations of links Study design Planned analysis Primary variable Secondary variable Adverse Events Serious events Study results Descriptive statistics Estimate and dispersion Counts number reporting event Number at risk (exposed) Presentation of the facts, no discussion or conclusions

Expectations for statisticians Ensure link between question (hypothesis), data collected and analysis methods and reporting (using standards) Plan for reporting at the study design stage Have analysis plan early Plan for handling issues (missing data, multiple testing, variance /covariance structure) Develop statistical models and programs Validate that statistical programs When data available Check model assumptions (distribution, dispersion, model fit) Provide data visualization tools to help with interpretation Provide statistical interpretation of the results

Statistical Contribution to CTD Plan for integrated analysis (what should be combined, how, why) Prepare subset analysis and interpretation May need separate analysis of results in population relevant to country where submitted (example – Chinese patients) Assist in quantifying Benefit/Risk Review documents and ensure that statements of statistical nature are phrased correctly and any interpretation or conclusion can be supported with the data Prepare for challenges to the data from the health authorities (what might they ask, why) Identify any bridging studies which might be needed (Japanese PK study)

Controversial Design or Analysis Plan to discuss with Health Authorities Need to supply Written question Documentation if appropriate (publication) Include justification for selection among options available May consult with Academic Statistical Expert Method of discussion US FDA – can request a meeting to discuss (Face to face or teleconference) Europe – request consultation – often written request & response

Some areas of interest Adaptive study design Not an issue for early studies (exploring dose response) Needs agreement if in the confirmatory development phase Dropping treatments Early termination (efficacy related) Application of new methods May need to be used as sensitivity analysis if not routine approach Multiple imputation methods for missing data New multiple testing methods Need to be able to define/support the level of control of error Data exploitation Looking for new insights given a large data set (hypothesis generation)