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The 800-Pound Gorilla: Taking on the Challenge of Globally Standardizing Adverse Event and Medication Reporting Deborah Harper, Pfizer Global Research and Development, Ann Arbor, MI Jennifer Tjernagel, Pfizer Global Research and Development, Ann Arbor, MI ABSTRACT When Pfizer merged with Warner-Lambert/Parke-Davis in 2000, work began to harmonize various processes across all sites. One of these efforts sought to standardize the collection and reporting of clinical data. The principle guiding the process was first to determine what needed to be reported, and then work backwards toward data collection. The initial step was to agree on a set of standard summary and listing tables for each data type defined in the Clinical Data Interchange Standards Consortium (CDISC). This presentation will discuss how we started, the process used, decisions made, resulting tables, and the SAS algorithms necessary to implement the standards for adverse events (AEs) and medications. Issues we addressed include common coding dictionaries, levels of reporting, definition of treatment, imputing dates, and the definition of treatment emergent signs and symptoms (TESS). Our presentation will focus on tables, and algorithms, which aid in the development of SAS code. INTRODUCTION In 2000 Pfizer merged with Warner-Lambert/Parke-Davis. It quickly became evident that the harmonization of various processes and definitions across all sites was essential. One of these efforts sought to standardize the collection and reporting of clinical data. This paper will detail some of the issues encountered by the Adverse Event and Medication teams. PROCESS BACKGROUND When the decision was made to commit to this global effort, a project team was formed that included representatives from multiple functions spanning all sites and divisions. The team’s responsibility was to design and create clinical data standards for the new Pfizer. The standards were to encompass a variety of clinical trial activities, from study protocol design to data analysis and summary tables. These standards needed to be flexible enough to apply to the whole spectrum of clinical trial types, from small Phase I studies to product defense studies. At the heart of the Pfizer data standards definition was the desire to maintain consistency with the standards defined by the Clinical Data Interchange Standards Consortium (CDISC). The CDISC Submissions Data Domain Models have been prepared by the CDISC Submissions Data Standards (SDS) team to guide the organization, content, and form of submission datasets for the 12 safety-related domains listed in the FDA guidance documents.1 Additional models (e.g., efficacy, pharmacokinetics) will be provided in the future by this organization. Pfizer’s clinical data standardization initiative was divided into several different phases. Process Team A was formed to define a standard template and develop instructions for the data standards documents. During the next phase, Process Team B began to define the processes by which these standards were to be created, modified and maintained in the future. Twelve sub-teams were formed concurrently to begin work on the standards. One team was assigned to each of the 12 domains defined in CDISC and the FDA guidance documents. This paper will focus on 2 of the 12 domains – AEs and Medications, which faced many of the same issues. We will discuss how the teams succeeded in resolving them. KEY ISSUES: CODING DICTIONARIES Working with a standard coding dictionary was one issue that was central to these 2 standard data types. The corporate decision was to use the MedDRA2 dictionary to code AEs, and the WHO-Drug3 dictionary to code medications. The MedDRA dictionary was new for every site, while the WHO-Drug dictionary was new to 3 of the 4 sites. Prior to the merger, each site used a different adverse event dictionary. As the dictionary choice ultimately affects the type of summary tables produced, the challenge for the teams was to come to a common understanding of the dictionary structures, and then decide whether new summary types were required to utilize the structure of each dictionary. Multi-axiality in the dictionaries was a new concept for many of the members. One investigator term for a medication or AE could now code to several different higher-level groups. For example, “aspirin” written on a case report form would code to “ACETYLSALICYLIC ACID” for the low level generic term in WHO-Drug, but ultimately would code to 3 different main groups in the highest level of the hierarchy: “ALIMENTARY TRACT AND METABOLISM,” “BLOOD AND BLOOD FORMING ORGANS,” “NERVOUS SYSTEM”. This will result in multiple counting of a drug among the different highestlevel terms (see Figure 1). FIGURE 1: Summary of Concomitant Medications Drug A N Drug B % N Drug C % N % Number of Subjects Number of Subjects With Any Concomitant Medication XX XX XX.X XX XX.X XX XX.X ALIMENTARY TRACT AND METABOLISM ACETYLSALICYLIC ACID BECOSYM FORTE CIMETIDINE TOCOPHEROL XX XX XX XX XX XX.X XX.X XX.X XX.X XX.X XX XX XX XX XX XX.X XX.X XX.X XX.X XX.X XX XX XX XX XX XX.X XX.X XX.X XX.X XX.X BLOOD AND BLOOD FORMING ORGANS ACETYLSALICYLIC ACID XX XX XX.X XX.X XX XX XX.X XX.X XX XX XX.X XX.X CARDIOVASCULAR SYSTEM LISINOPRIL XX XX XX.X XX.X XX XX XX.X XX.X XX XX XX.X XX.X NERVOUS SYSTEM ACETYLSALICYLIC ACID MEDINITE PHENOBARBITAL XX XX XX XX XX.X XX.X XX.X XX.X XX XX XX XX XX.X XX.X XX.X XX.X XX XX XX XX XX.X XX.X XX.X XX.X SENSORY ORGANS TOBRADEX XX XX XX.X XX.X XX XX XX.X XX.X XX XX XX.X XX.X VARIOUS XX XX XX.X XX.X XX XX XX.X XX.X XX XX XX.X XX.X GENERAL NUTRIENTS XX XX DATA COLLECTION TOOLS or CRFS Another decision all teams faced was the creation of a common data collection tool (DCT), a term inclusive of case reporting forms (CRFs), electronic data capture screens, and so forth. The adverse event and medication teams faced the additional challenge of determining a process for bringing the data in-house. Some sites collected this information as “visit” based, others used the “log” method, which simply stated means that the form remains in the field from event beginning to end, or study completion, which ever comes first. Adverse Events and Medications ultimately chose the “log” method and developed a supporting process. INTENT-TO-TREAT (ITT) vs. ACTUAL TREATMENT AND TREATMENT EMERGENT SIGNS AND SYMPTOMS It was decided in the early stages of the treatment data standard development to use the ITT method for analysis. In the simplest of terms, ITT means a patient is analyzed as being under the influence of randomized therapy once therapy has begun until study end regardless of actual therapy (e.g. interruptions and unplanned titration). It was up to each team to assess the impact of this decision on their data standard. Legacy Warner-Lambert and legacy Pfizer used different methods and criteria for determining if a given adverse event was to be considered treatment-emergent. According to ICH guidelines, “adverse events (AEs) that are treatment emergent either start or worsen after study drug administration begins”4. A single method for determining TESS must be developed in order for data from multiple studies to be comparable for submission, marketing and product defense purposes. For adverse events ITT analysis greatly simplified the existing calculation that rendered the determination of Treatment Emergent Signs and Symptoms. DATE ESTIMATIONS To estimate or not to estimate is always a dilemma for those reporting clinical data. It was decided as rule of thumb that data should stand on its own and estimations should be done sparingly, if at all. The exception was dates. Each standards team had to decide and justify estimating dates, and create rules regarding date estimation independently. There is always the possibility of not associating a subject with treatment due to a missing date. The adverse event group decided to estimate start dates only. See the algorithm section for additional date considerations deliberated by the medications team. RESULTING TABLES The process started with reviewing current guidance documents of regulatory bodies and determining which presentations were essential in proving the safety of our treatments. We worked backwards from data presentations to data sets, then on to the DCT data validation and protocol language. The goal in developing the summary tables was to provide a format that would meet the needs of 80% of the projects. The adverse event summary table (Figure 2) shown below is an example of one of the data presentations defined for AEs. Figure2: Summary of Adverse Events System Organ Class Preferred Term TESS Adverse Events Number of Subjects (%) with Adverse Events Treatment A Treatment B N=59 N=59 Total Number of Subjects with AEs Body as a whole Headache Abdominal pain Accidental injury Asthenia Back pain Chest pain Flu syndrome Chills Infection Neck pain Pain Face edema Generalized edema Cardiovascular system Postural hypotension * asterisked and italicized items are optional * Total N=118 39 (66.1) 45 (76.3) 84 (71.2) 23 (39.0) 7 (11.9) 4 (6.8) 2 (3.4) 3 (5.1) 3 (5.1) 0 (0.0) 3 (5.1) 0 (0.0) 6 (10.2) 1 (1.7) 2 (3.4) 1 (1.7) 1 (1.7) 16 (27.1) 6 (10.2) 2 (3.4) 2 (3.4) 2 (3.4) 2 (3.4) 2 (3.4) 2 (3.4) 1 (1.7) 1 (1.7) 1 (1.7) 1 (1.7) 0 (0.0) 0 (0.0) 39 (33.1) 13 (11.0) 6 (5.1) 4 (3.4) 5 (4.2) 5 (4.2) 2 (1.7) 5 (4.2) 1 (0.8) 7 (5.9) 2 (1.7) 3 (2.5) 1 (0.8) 1 (0.8) 3 (5.1) 0 (0.0) 2 (3.4) 1 (1.7) 5 (4.2) 1 (0.8) Two different medication summary tables were defined in the data standards. The first summarized the data first by highest-level term, then by low-level generic term within each highest-level term (Figure 1). As previously mentioned, this table may categorize a generic term in several main groups. An option was added to the standards to allow therapy teams to choose primary highest-level terms for summarization. This would eliminate multiple counting of a drug, if desired. The second available medication table type is summarized just by the low level generic term (Figure 3). Figure 3: Summary of Concomitant Medications, Low Level WHODrug Term only Drug A N Drug B % N Drug C % N % XX.X XX XX XX.X Number of Subjects Number of Subjects With Any Concomitant Medication XX XX XX.X XX XX ACETYLSALICYLIC ACID BECOSYM FORTE XX XX XX.X XX.X XX XX XX.X XX.X XX XX XX.X XX.X CIMETIDINE GENERAL NUTRIENTS LISINOPRIL XX XX XX XX.X XX.X XX.X XX XX XX XX.X XX.X XX.X XX XX XX XX.X XX.X XX.X MEDINITE PHENOBARBITAL XX XX XX.X XX.X XX XX XX.X XX.X XX XX XX.X XX.X TOBRADEX TOCOPHEROL XX XX XX.X XX.X XX XX XX.X XX.X XX XX XX.X XX.X ALGORITHMS The standardization of data sources is difficult when trying to create a standard that will accommodate all the needs of investigational studies, post marketing, and product defense. A three- tier approach to managing data sets was devised to allow the collection of data from a variety of sources. The harmonization and standardization of data occur in the intermediate data set. Therefore, data manipulation and calculations are performed after the creation of the intermediate data set, or at the time of data presentation generation. An example is a portion of the algorithm created to apply MedDRA fields to the intermediate listed below: AEDECOD1 = MedDRA: Lowest Level Term AEDECOD2 = MedDRA: Preferred Term AE DECOD3 = High Level Term AE DECOD4 = High Level Group Term AEBODSYS = System Organ Class There were two primary algorithms for the medication data standard. The first considered data estimation. For medications it was decided to estimate both start and stop dates if the date was partially missing. Completely missing dates were not estimated. For example, for partially missing dates, the most conservative date was estimated: For Start Date – assume 1st day of month or 1st month of year. E.g. xxMAR2001 -> 01MAR2001, 22xxx2001-> 22JAN2001 For Stop Date – assume last day of month or last month of year E.g. xxMAR2001 -> 31MAR2001, 22xxx2001-> 22DEC2001 The other algorithm for dates determined whether a medication was a prior (P) or a current (C) medication, or both. Typically a separate summary table will be produced for each of these 2 categories. After the date estimation was completed, the rules illustrated in Figure 4 below could be applied. Day 1 is defined in this example as the first day the patient took study treatment. For the purposes of the rules, a date is “given” if either a complete or partial date was provided. A date is missing if the date was completely missing, and hence not estimated. Figure 4: Rules for Determining Prior or Concurrent Medications (or both) for Summary Purposes DAY 1 PRIOR (P) CONCURRENT (C) Designation: Start and Stop Dates given Period of Medication P 3 WHO Collaborating Centre for International Drug Monitoring, The Uppsala Monitoring Centre, Stora Torget 3, S-753 20 Uppsala, Sweden, www.who-umc.org 4 ICH Harmonized Tripartite Guideline: Clinical Safety Data Management: Definitions and Standards for Reporting, October 27, 1994; published in Federal Register, March 1, 1995, 60 FR 11284 P/C C Single event on Day 1 Start Date missing, Stop Date given P/C P P/C ACKNOWLEDGMENTS ICH Harmonized Tripartite Guideline: General Considerations for Clinical Trials; July 17, 1997 C Start Date given, Stop Date missing P/C P/C C Start Date missing, Stop Date missing Algorithms - prior-con.vsd, Page 1 of 1 © Pfizer Inc., 17 September 2001 P/C DESIGNATING CONCOMITANT MEDICATIONS AS 'PRIOR' OR 'CONCURRENT' LESSONS LEARNED One of the reasons this large global project succeeded was the commitment at the highest level of management. This project was recognized as essential to the future of the business, and priorities were adjusted accordingly. Face to face meetings with the members of the various teams were conducted, and dedicated resources had blocks of protected time to work on this initiative. Overall the high level of commitment led to a higher quality product in a short period of time. In order for this approach to succeed, it was critical that members at each site suspend their preference for their historical processes, and instead focus on creating the best practices for all sites. The willingness of the teams to embrace this mindset was crucial to effective decision-making, and fostered an environment of learning. The participation of clinical colleagues is crucial in setting standards, not only on the data standards teams but also at the highest level of sponsorship. Because processes will be changing and the majority of colleagues were not directly involved in the decision making, robust communication and clear definition of the rationale for each decision is essential. This will assist colleagues in accepting the new processes and integrating them into their business CONCLUSION Pfizer invested a tremendous amount of time, effort, and capital into this global standardization project believing that the rewards of standardizing the analysis, reporting, and collection of data in the future will be well worth the cost. It also gave the organization the opportunity to drill down into the details and ensure that the Pfizer data definitions and calculations were consistent across the company. These standards put Pfizer in a position to analyze and report data the instant the database is finalized. REFERENCES 1 2 http://www.cdisc.org http://www.meddramsso.com ICH Harmonized Tripartite Guideline: Structure and Content of Clinical Study Reports; November 30 1995 (provides guidance regarding concomitant medications and recording of information, as medications being taken at time of AE vs. treatment administered to treat the AE; pp 9, 16, 24) ICH Harmonized Tripartite Guideline: Clinical Safety Data Management: Periodic Safety Update Reports for Marketed Treatments; November 6, 1996 Defining Treatment-Emergent Adverse Events with the Medical Dictionary for Regulatory Activities (MedDRA) Mary E Nilsson, MS Senior Statistician, Statistical and Mathematical Sciences Stephanie C. Koke, MS, Associate Communications Consultant, Global Scientific Information and Communications. Lilly Research Laboratories, Indianapolis, Indiana, Drug Information Journal Vol. 35 pp.1289-1299 2001 CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the authors at: Jennifer Tjernagel Pfizer Global Research and Development 2800 Plymouth Road Ann Arbor, MI 48105 Work Phone: (734) 622-3599 Fax: (734) 622-2017 Email: [email protected] Deborah Harper Pfizer Global Research and Development 2800 Plymouth Road Ann Arbor, MI 48105 Work Phone: (734) 622-7657 Fax: (734) 622-5622 Email: [email protected]