By Richard K. Rondel, Sheila A. Varley, Colin F. Webb
Widely revised and up-to-date, with the addition of recent chapters and authors, this long-awaited moment version covers all points of medical facts administration. Giving information of the effective scientific information administration systems required to fulfill either company ambitions and caliber audits through regulatory professionals, this article is well timed and a big contribution to the literature.
The volume:
* is written by means of recognized and skilled authors during this area
* presents new ways to significant issues in scientific information management
* comprises new chapters on structures software program validation, database layout and function measures.
It can be precious to somebody within the box in the pharmaceutical undefined, and to all biomedical execs operating in scientific learn.
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Extra resources for Clinical Data Management
Example text
Investigator site processes for data generation and capture are usually assessed by clinical staff, with the exclusion of data management staff. It would be preferable to include data management staff in site pre-evaluation activities so that data management plans are written with consideration to the investigating sites’ data management processes. 01 PICAS WIDTH 40 PICAS THE ICH AND ITS IMPACT 35 amendments and revised case report forms) must be made to enable simple reconfiguration of the changes at a later date.
For electronic records the controls for closed and open systems are specified. Expectations include appropriate validation of the systems used; inclusion of an audit trail; appropriate security systems; authorised access; implementation of quality control; users are appropriately trained; users have access to written policies and procedures governing the use of the system, revision and change control procedures are in place. Sponsors who take advantage of this practice will have to consider its application to remote data entry systems and electronic transfer of data supporting a clinical trial.
The classification of protocol deviations must be defined in advance in the clinical data management plan. As stated earlier the ICH guideline E3 gives more guidance on what protocol deviations must be discussed in the final study report. The information collected on this topic is a powerful study management tool and should be shared with the study team on a regular basis. 3 ‘Missing values represent a potential source of bias in a clinical trial. ’ This point emphasises the importance of data coding conventions, the monitoring of protocol deviations and the importance of raising data queries to the site (rather than assuming data are missing) when it is not clear if the data were generated or not.