An FDA Statistician’s Perspective on Standardized Data Sets Steve Wilson & Cynthia Liu CDER/OPaSS/OB/Division II CDISC Initiatives – Impact on Electronic Data Submission FDA/Industry Statistics Workshop September 15 - 16, 2005 Disclaimer: The views expressed in this presentation are those of the speaker and do not necessarily represent those of the Food and Drug Administration

Outline Background and Regulatory Guidance 2004 FDA/Industry Workshop -- William J. Qubeck, Beyond the CDISC SDTM V3.1 Model: Statistical & Programming Considerations Cynthia Liu -- Review Experience

Background: Why Standards? Improve patient safety and reduced costs by reducing time to market for safe and effective treatments by Improving efficiency of evaluation of safety and efficacy of investigational treatments Facilitating communication between regulatory authority and applicant Facilitating development of efficient review environment (e.g., access to data, orientation, redundancy, training, analysis tools) Levin, 2005

Background: Why Standards? Improve patient safety and reduced costs by reducing time to market for safe and effective treatments by: Improving efficiency for clinical research Facilitating design and conduct of clinical trials Facilitating communication between researchers and study sponsor (e.g., between CRO and drug company) Integration with the electronic health record Levin, 2005

Background: Why Standards? Facilitating communication between regulatory authority and applicant Facilitating development of efficient review environment (e.g., access to data, orientation, redundancy, training, analysis tools)

Regulatory Guidance Guidance, 1999 Study Data Specifications, 2004 Providing Regulatory Submissions in Electronic Format — NDAs Study Data Specifications, 2004 Submit Both SDTM and Analysis Data “Study Data Tabulation Model (SDTM) developed by the Submission Data Standard working group of the Clinical Data Interchange Standard Consortium (CDISC)”

Study Data Specifications Individual subject data listings Data tabulations Data tabulations datasets Data definitions Data listing Data listing datasets Analysis datasets Analysis programs

William J. Qubeck, IV MS, MBA 2004 FDA/Industry Workshop -- Beyond the CDISC SDTM V3.1 Model: Statistical & Programming Considerations William J. Qubeck, IV MS, MBA Electronic Submissions Data Group Leader Global Clinical Data Services, Pfizer Inc. CDISC SDTM Version 3.1 Interventions Events Findings Implementation for Pfizer Submission – First SDTM data submitted to CDER

Submission Data Processes FDA PFE CRTs (Raw) Extraction &/or Transformations DB Clinical Algorithms Analysis eSub CDISC Pilot FDA Extraction &/or Transformations CRTs (Raw) DB Clinical Algorithms Analysis eSub SDTM Qubeck, 2004

First FDA Statistical Review Experience of CDISC SDTM Submission: Cynthia Liu Division of Biometrics II / CDER

The first statistical reviewer at FDA to work with the new data format (Study Data Tabulation Model) and the new tool (Web Submission Data Manager). Terrifying or exhilarating?

Preparation for the Review Understand the submission (indication, drug, study design, endpoints, analysis methods, etc.) See if data are accessible Usual format - through EDR (Electronic Document Room) SDTM format - through WebSDM (Web Submission Data Manager) See if data are sufficient (primary, secondary, LOCF indicator, etc.)

Preparation for the Review (continued) Attend the first training class of WebSDM held by Lincoln Technologies Receive a training guide Receive an introductory training to fundamentals of the SDTM format Receive a demonstration and hands-on training on WebSDM tool developed for FDA to review the standardized data Helpful, simple, and straightforward

Comparison Between the Analysis Files (customized data sets) and SDTM Files Use of Sponsor Supplied Analysis Files: Find the data files containing efficacy variables Read data definition files (define.pdf) to understand variable names, types, formats, and codes Write SAS codes to perform statistical analyses for the primary and secondary efficacy variables (e.g., PROC MEANS, PROC MIXED, PROC FREQ, and PROC LOGISTIC, etc.)

Comparison Between the Analysis Files (customized data sets) and SDTM Files (continued) Use of Downloaded SDTM Files from WebSDM: Download the data file containing efficacy variables (ABC.XPT - huge file, 260K+ rows) Do extensive data manipulation in the SAS Data Step (will explain in the next few slides) Write SAS codes to perform statistical analyses for the primary and secondary efficacy variables (e.g., PROC MEANS, PROC MIXED, PROC FREQ, and PROC LOGISTIC, etc.)

The extensive data manipulation involved the following: For the primary efficacy variable Select the baseline, post-baseline, and % change from baseline for the primary efficacy variable Select appropriate visit numbers for analyses Delete duplicate or invalid data - measurement collected more than once for the same visit window and no indication of which data to be used for analyses Transpose the vertical data format to the horizontal one by subject and visit

Downloaded SDTM Efficacy Data Duplicate data Four out five studies did not achieve the difference planned, which was supposed to be the minimum clinically meaningful difference. Invalid data

The extensive data manipulation involved the following: For the primary efficacy variable (continued) Extract data for each visit Create a new data set by merging data of each visit so that last valid observation was able to be carried forward (LOCF) For the secondary efficacy variable Create a categorical variable (z) based on % change from baseline (y) for responder analysis as defined by the sponsor (e.g., if y  0 then z = 1; else z = 2)

Summary of My Experience If data files are structured and documented well by the sponsor, it is easier to use analysis files than SDTM files for statistical analyses, because less programming is required. However, contents and numbers of analysis files submitted vary from sponsor to sponsor, or even within the same sponsor. Because of standardized domain names, variable names, and formats, reviewers can find and understand data more easily; and therefore, minimize the learning curve.

Summary of My Experience (continued) Reviewers can download data of interest through WebSDM for exploratory analyses, instead of waiting for the sponsor’s response (time saving). Standards are a good thing - imagine no standards for traffic lights when we drive and walk! One last note - please talk to statistical reviewers and submit analysis files, particularly the ones for the primary and important secondary variables.

Am I still terrified? Not anymore. Am I still exhilarated? Certainly! Acknowledgements Dr. Steve Wilson Dr. Todd Sahlroot Dr. Ed Nevius Thank you very much for your attention.