Services

1. Database Designing
2. Data collection
3. Data entry
4. Data Validation
5. Data cleanup
6. Data Analysis
7. Data Reporting
8. Data Presentation

Design CRF/eCRF to collect the data as specified by the protocol.
Design of clinical databases; laboratory databases, validation, derivation procedures and data  transfer specifications

To design the CRF along with protocol to assure collection of only the data the protocol specifies.
To design the CRF with the primary safety and efficacy endpoints in mind as the main goal of data collection.

 Assure compliance with 21CFR 18 and consistency with FDA Guidance – Computerized Systems Used in Clinical Trials.
 Ensure that User Acceptance Testing is completed prior to implementation and deployment to sites.
 Ensure that software systems validation is scheduled and completed prior to EDC study
implementation. 
Ensure that your organizations quality standards support the utilization of automated data capture, management and archival.
Ensure requirements for data transfers and integration with other systems are defined.
Ensure sites have access and control of data up to database lock.
Ensure that access of data is limited to authorized individuals.
Ensure the availability of technical support for users.
Ensure training is provided for all users of the EDC system.
Ensure reinstallation qualification is completed for all repairs and updates to the EDC systems and applications.

 To plan studies to avoid “last minute” system modifications that introduces errors and complexity to the EDC systems.

Develop e-CRFs or data collection tools with teams of individuals frommonitoring, data management, statistics, regulatory affairs, and medical, ensuring adequate attention to the collection of safety data.

To ensure that systems are user-friendly and flexible for data entry
To ensure that adequate data validationprocedures and query management tools are built into the EDC study software
To ensure that data can be traced from acquisition to report and analysis files through easily accessible audit trials.
To ensure ease and quality of all data transfers and integration with other databases by testing data transfers prior to deployment of EDC systems.

To ensure processes are defined to integrate laboratory and other non CFR data with the data from the eCRF.
To ensure all User Acceptance Tests are documented.
To define change control procedures for all “user-configurable procedures” such as
edit specifications.
To automate generation of reports on metrics and project status to facilitate project /site / patient management.

 During the conduct of a clinical trial, store all original data collected (e.g., Case Report Forms and electronic laboratory data) in secured areas such as rooms or file cabinets with controlled access (e.g., Locks).

These original documents are to be considered part of the audit trail for tracing back to the source data and should be protected and controlled asrigorously as the electronic audit trail of database modifications or backup procedures.

Document the procedures for granting access to database servers, establishing system controls and assigning passwords.  This process is especially important in a trial where the original data collection is done electronically and no paper backup exists.

 Backup copies can be made easily and frequently. For example, paper documents should be scanned and electronically archived.
 Database design specifications - Documentation of the table definitions used to build the study database and file structure.
 Raw data - The final raw data files preserved within the study database format and all original data transfers in their raw format.
 Audit trail - A complete electronic audit trail documenting all modifications by date, time and user identification.
 Final data - It is critical to preserve the final data in a standard file format (e.g., ASCII, SAS transport) so that it can be easily accessed, reviewed or migrated to another system.

Original study documents – The original and/or scanned images of all original documents. These may be archived separately in a central records facility if necessary.

Database Closure - Documentation of each database lock and unlock describing the time and conditions surrounding those procedures Utilize open formats whenever possible for archival, storage and transport of data (e.g., ASCII, SAS Transport, Portable Document Format (pdf), CDISC ODM Model).
Adherence to this practice enables access to the data by multiple systems or reviewers, currently and in the future.

 Validate database management systems in their local environment  prior to use.  Test the set-up and programming of each study managed within the validated database management system to assure that any new code generated or new system functionality used for the study, works according to the user's specifications.

Define the testing methodology, scope problem reporting and resolution, test data and acceptance criterion within  the Test Plan.

 Data entry screen testing to assure that data are mapped to intended database structures

Validation of any generic integrity constraints or data checking  routines that execute during data  entry (e.g. range, date, format,  coding, field discrepancies)
  
Testing of data verification functions such as second entry verification, file comparison and batch verification
  
Batch data transfer to the clinical trial database from separate data entry systems (e.g. electronically transferred data or remote data entry systems)
  
Prospectively written design specifications that describe what the software is intended to do and how it is intended to do it (A version controlled system manual can serve
as design specifications.)
  
A test plan based on the design specification and the intended use of the database management system that outlines both how the testing  will be conducted and the criteria  for acceptance or rejection of the software based on testing outcomes
  
Results documentation that describes why the software will be accepted or rejected based on specific test results.  Appropriate review and approval documents.

 Independent double data entry with a third person compare where two people enter the same data independently and a third person resolves any discrepancies between
first and second entry;   

Double data entry with blind verification where two people enter  the data independently and any discrepancies are resolved during second entry
 
Double entry with interactive verification where the second entry operator resolves discrepancies between first and second entry and is aware of the first entered values

Single entry with a manual review

Utilize written procedures that describe the data processing steps and required quality level. Ensure enough specificity to reproduce the analysis database from the source

Ensure that all procedures, guidelines, working practices, or references are current and available to - Apply "Quality Control . . . to each stage in the data handling process to assure that the data are reliable and processed correctly."

Provide descriptive guidelines for staff who write queries for investigator sites.

Address the purpose, characteristics and complexity of each study in the data cleaning
procedures.

Define the list of critical variables (usually those of primary and secondary safety and efficacy and subject identifiers) before the data cleaning activities are defined.
  
Monitor data processing production functions adequately to assure stable and desirable quality levels consistent with the needs of the trial.

Provide easy access for all employees to documentation for all current procedures, guidelines, working practices or references for the tasks they will perform.

Inform sites of timelines for data entry, running data checks and replicating the data.
Establish database quality criteria. Establish a quality control plan with the study team that appropriately addresses the primary efficacy data.

Processes for managing the release of multiple versions of the same dictionary over a short period of time, handling different dictionaries or versions that have been used and
integrating data coded with different dictionaries or versions must be established

Select appropriate dictionaries that meet project requirements.    Install and securely maintain dictionaries.

Implement an audit trail for all changes to the coding dictionary.

Identify dictionary and version in clinical study reports and integrated summaries.

Store all versions of dictionaries for future reference.

Select an auto-encoder to facilitate the consistent use of dictionaries.   Include version of dictionary in metadata.

Store data from all coded dictionary levels whether or not all are initially analyzed or reported.

Establish process for evaluating a  change in a dictionary or version.   Allow search for coded terms to evaluate possible effect of a version change.
Use the same version of a dictionary for coding in combined (integrated) studies.

Clinical data archiving includes the planning, implementing and maintaining of a repository of documents and records that contain clinical data together with any
interpretive information from a clinical trial.

The clinical data archive should include a centralized table of contentfor all studies.

The accessibility of the clinical data archive should be tested following every major upgrade of the active clinical data management system. documents and records that contain clinical data together with any interpretive information from a clinical trial.

 All clinical data, metadata, administrative data and reference data should be maintained in an industry standard, open systems format, such as CDISC ODM.
 
An electronic repository links all study components including the clinical data, CRF (Case Report Form) images in PDF form, program files, validation records and regulatory documentation.

 The audit trail should be stored in open format files in a secure file system location.   Copies of all user and system documentation for any applications used to collect or manage clinical data are retained in the corporate library or archive facility.
 
Reports describing the study metadata, including data structures edit check descriptions; lab-loading  specifications are printed and stored in a central document library.

The study validation binder should be included in the document library   System security reports, including  user listings, access rights and the  dates of authorization, should be
printed and filed or scanned.

 The edit check archive should include all program code for edit checks, functions and sub-procedures together with a copy of the version control information.
 
Paper CRFs should be scanned and  indexed. If an EDC, Electronic Data Capture, system is used; entry screens should be archived as PDF.