- Email: [email protected]
P17 Issues in using a university data coordinating center for an industry sponsored clinical trial
Abstracts
81S
of participants completing specific follow-up visits to an identification of what participants within each center have missing forms. Application of the macro language is well suited to this type of trial where the same variables are measured at multiple points in time, and the processing of data is done in exactly the same manner for each clinic. By changing the definition of parameters passed to the macros, the macros are generally transportable to other clinical trials.
P16 AN AUTOMATED EDITING PROCESS FOR RESEARCH DATABASES Pamela S. Moke and C. Hendricks Brown
Jaeb Center for Health Research Tampa, Florida Editing of clinical trial databases is a complex and time consuming process involving the detection, evaluation, and tracking of possible errors as well as updating datasets with confirmed edits. Not only must the database be updated, but a record of all changes that have been applied to the data must be maintained as part of the trial's documentation. As part of the Longitudinal Optic Neuritis Study (LONS), we developed a UNIXbased editing process that evaluates data received, generates edit messages for the source clinics, and provides a paper trail of changes when updating the existing database. The primary component of the process is a generic C program that generates SAS code based on a given study's edit criteria. The resulting SAS program code then outputs data that are processed with nroff/troff tormats and UNIX tools to create a uniquely numbered, traceable, query message tor the source clinic. Specifications tor the program are listed in a simple ASCII text file and can include the standard editing steps (valid values, range checks, etc.) as well as conditional statements (e.g., comparison against a master database), edit message information (date, form number, variable label, variable location on the form, etc.), and specific SAS code to be executed during the editing procedures. We have now extended this editing process to other studies and will present examples of program input and output including edit specifications, query messages, and update reports. The program is adaptable for use by interested parties. P17 ISSUES IN USING A U N I V E R S I T Y DATA C O O R D I N A T I N G C E N T E R F O R AN I N D U S T R Y S P O N S O R E D C L I N I C A L T R I A L
Murray D. Barnhart
Bristol-Myers Squibb Company Prh~ceton, New Jersey. The Bristol-Myers Squibb Company has sponsored four large clinical trials where universities acted as the Data Coordinating Centers. Issues involving data management were common during the conduct and reporting of these trials, often because of the need to preserve both the scientific and regulatory integrity of the study. Two of the trials were conducted outside of the United States and added distinct challenges to the process. Data Management topics to be discussed include processing serious adverse reactions, compatibility of data with other studies conducted by the Sponsor, availability of blinded data to the Sponsor prior to the end of the trial and logistics in the preparation of registrational dossiers.
81S
of participants completing specific follow-up visits to an identification of what participants within each center have missing forms. Application of the macro language is well suited to this type of trial where the same variables are measured at multiple points in time, and the processing of data is done in exactly the same manner for each clinic. By changing the definition of parameters passed to the macros, the macros are generally transportable to other clinical trials.
P16 AN AUTOMATED EDITING PROCESS FOR RESEARCH DATABASES Pamela S. Moke and C. Hendricks Brown
Jaeb Center for Health Research Tampa, Florida Editing of clinical trial databases is a complex and time consuming process involving the detection, evaluation, and tracking of possible errors as well as updating datasets with confirmed edits. Not only must the database be updated, but a record of all changes that have been applied to the data must be maintained as part of the trial's documentation. As part of the Longitudinal Optic Neuritis Study (LONS), we developed a UNIXbased editing process that evaluates data received, generates edit messages for the source clinics, and provides a paper trail of changes when updating the existing database. The primary component of the process is a generic C program that generates SAS code based on a given study's edit criteria. The resulting SAS program code then outputs data that are processed with nroff/troff tormats and UNIX tools to create a uniquely numbered, traceable, query message tor the source clinic. Specifications tor the program are listed in a simple ASCII text file and can include the standard editing steps (valid values, range checks, etc.) as well as conditional statements (e.g., comparison against a master database), edit message information (date, form number, variable label, variable location on the form, etc.), and specific SAS code to be executed during the editing procedures. We have now extended this editing process to other studies and will present examples of program input and output including edit specifications, query messages, and update reports. The program is adaptable for use by interested parties. P17 ISSUES IN USING A U N I V E R S I T Y DATA C O O R D I N A T I N G C E N T E R F O R AN I N D U S T R Y S P O N S O R E D C L I N I C A L T R I A L
Murray D. Barnhart
Bristol-Myers Squibb Company Prh~ceton, New Jersey. The Bristol-Myers Squibb Company has sponsored four large clinical trials where universities acted as the Data Coordinating Centers. Issues involving data management were common during the conduct and reporting of these trials, often because of the need to preserve both the scientific and regulatory integrity of the study. Two of the trials were conducted outside of the United States and added distinct challenges to the process. Data Management topics to be discussed include processing serious adverse reactions, compatibility of data with other studies conducted by the Sponsor, availability of blinded data to the Sponsor prior to the end of the trial and logistics in the preparation of registrational dossiers.