Speakers' Abstracts ~International Journal of Antimiorobial Agents 26S (2005) S1 $63 This workshop is oriented towards investigators, data managers, data clerks, data monitors, oneology nurses, and other members of the clinical trial team who are involved in oneology trial/research. Two major topics will be discussed during the workshop: (1) Practical guidelines on how to efficiently manage source documents; and (2) practical guidelines on how to maintain data collection fomls or records. Prior to discussion of tile two major topics, an overview of on - Hid off site data collection procedures and some definitions of terms will be presented. Some ease examples will also be shown during the presentation.
W2.3 Basic Data Elements in Cancer Clinical Trials Julia CHALLINOR. University of California, San Francisco, USA The outcome of any research study is dependent on tile quality of tile data that is collected for analysis. Appropriate interpretation of tile results of a research study is critically dependent on how tile data was collected, entered into the database and subsequently analyzed. High quality basic data collection elements are essential for appropriate treatment decisions. In the United States the National Cancer Institute's "Cancer Therapy Evaluation Program" (CTEP) funds a national program of cancer research and sponsors clinical trials to assess proposed anti-cancer agents. Information based on tile CTEP "hlvestigators Handbook" will be presented as an example of tile criteria for basic data collection elements. Potential data collection errors and examples of such will be discussed in detail. Strategies for preventing data error will be outlined.
W3.2 E r r o r D e t e c t i o n a n d Correction in Data Collection Julia CHALLINOR. University of California, San Francisco, USA In recent years, significant international efforts have been made to pursue documentation of health care progress in developing countries thi'ough formalized data collection and analysis. Funding has been provided to create databases for statistical analysis of healthcare initiatives in these countries. However, the outcomes of data analysis, subsequent conclusions and patient care treatment modifications, depend on tile accuracy of tile data that is accumulated. Data erroi, which may lead to mistaken conclusions and inappropriate change in treatment, continue to challenge the researchers and statisticians who conduct these investigations. The legibility, formality of charting, organization of primary source documents, and interpretation of document iiffomlation can vary widely among departments, medical centers and collaborating countries. Cleating a formalized system for early error detection, reporting, and cmrection within a healthcare group is essential to ensure accurate data for accurate analysis. Clinical trials for cancer treatment are a particularly complex example of patient care. The vast amount of information that is collected during the years of patient treatment creates a critical database of infomlation for CUlrent mid future patient treatment. It is important to recognize tile difference between a "mistake" and "misconduct" in data collection mid data entiT?. A detailed description of potential errors in data collection and recommendations to create formalized methodologies to detect and correct such errors will be presented.
W3. Basic Data Management in Cancer Research IIh Data Quality Assurance (Co-sponsored by
the International Network for Cancer Treatment and Research~Philippine Society of Pediatric Oneology/Philippine Society of Oncology) W3,1
Requirements for Quality A s s u r a n c e Melissa ADDE. International Network for Cancer Treatment atwl
Research (1NCTR), Brussels, Belgium Quality assurance consists of planned and systematic actions that are established to ensure that a clinical trial is conducted and that the data are generated, recorded and reported in compliance with standards of Good Clinical Practice (GCP) and tile applicable legalatoC¢ requirements. Standard operating procedures that axe developed by both tile sponsor and investigational site level have an important role in assuring quality. The purposes of clinical trial monitoring are to verify that the rights of human subjects participating in research are protected, that the data are accurate, complete and verifiable from source documents, mid that tile trial is being conducted in compliance with tile protocol document, with GCP standards, and with applicable regulatory requirements. A study monitor has tile responsibility for ensuring that the trial is conducted and documented properly by carrying out visits and by performing certain procedures during each visit. Monitoring visits are typically conducted prior to the commencement of a trial, thi'oughout tile course of tile trial and again at tile completion of a trial. Audits are independent mid separate from monitoring and axe performed to evaluate trial conduct mid compliance with the protocol, standard operating procedures, good clinical practice, and applicable regulatory requirements. The role of both the site data manager and the off site and/or sponsor data manager in overseeing the processes related to quality aasurance will be described in this presentation.
Improving Quality Assurance by Using Information Technology Lolita LANTICAN. International Network for Cancer Treatment ami
Research (INCTR), Brussels, Belgium Data managers are challenged to develop faster ways of producing clean databases, thus enabling speedier trial results, and to improve data quality. With the current technological advances in data management, data managers can achieve their goals by proper use and good knowledge of information technology. Tile objectives of this wm'kshop are: (1) To discuss tile different data validation processes from data collection to data tables/listing; and (12) to demonstrate tile practical use of infomlation technology on different aspects of data management by using case examples. Specifically, participants will gain insights/knowledge on: Data validation procedures: This outlines the different steps to follow in order to improve tile timelineas and quality of data collected. D a t a checks plan: This is a checklist of all possible data el:rors (i.e., m i n i m u m checks or extensive checks). Information technology systems/programs: These are computer pro grams or 1T systems developed and used by INCTR in its daily data management activities such as: • On-line Registration System (ORS). A system allowing investigators, data mmlagers on site and other site users to iegister eligible patients on line (24 hours from Monday thnt Sunday) using tile interaet. • CRF tracking system. A system allowing off-site data managers to keep track of missing or overdue ease report forms (CRFs) in order to improve data timeliness. • Remote Data Entry System (RDES). A system used to enter data on site using interaet. Built-in data validation lades with m i n i m u m data checks like range checks, date fro'mat and date inconsistencies ensure that clean data are collected. • Data validation program. By running this program, a query is automata rally displayed and this query can be sent electronically to participating sites for source data verification (SDV) or confirmation. Queries are data el:rots or data inconsistencies in tile database.