Wednesday, February 26, 2020

The Essentials of Clinical Trial Data Management

Accurate and high-quality clinical data is of critical importance for every CRO medical company.

Effective clinical trial data management involves accuracy in data collection, data entry, data reporting and data validation.

Establishing and maintaining high standards of quality requires consistency in implementation across individuals and teams in the CRO medical organization.

Clear communication in data management can prevent costly mistakes by staff which can put the clinical trial at a huge risk.

The following clinical trial data management elements can help in improving a CRO medical company’s data management standards at every stage of the clinical trial.

Accurate and Relevant Data Collection

Standard practices need to be established by CRO medical companies to produce accurate and relevant data.

The methodologies used for clinical trial data management should be targeted towards the objectives of the clinical trial, resulting in an improvement in data quality.

This process will also help in the elimination of non-critical data. This is important because minimizing the risk of verifying such data lowers risk factors during endpoint analysis.

With the right guidance, clinical data integrity can be vastly improved and data quality variation among the individuals and teams in CRO medical companies can be significantly reduced.

Critical data points identification

Critical data points should be identified at the beginning of a clinical trial process. This requires determining which data needs to be measured in relation to achieving the end goal.

Fundamentally, clinical trial data management appears straightforward but the collection of non-critical data for purposes like patient safety can pose significant challenges.

It requires time and effort by the clinical trial data management team to ensure this data meets required qualified standards.

Aside from being able to identify additional data points, proper Standard Operating Procedures (SOPs) helps reduce the effort of data managers working with large volumes of data. This ultimately improves data quality.

Developing Standard Operating Procedures (SOPs)

Accurate and quality collection of clinical trial data helps minimize time spent in identifying and rectifying inaccurate data.

This requires a clear definition of responsibilities for each role as well as organizational practices.

This ensures uniformity in operations minimizes the chance of errors occurring and makes it easier to identify the cause of errors when they occur.

Standard Operating Procedures (SOPs) should be developed in collaboration with staff members.

This ensures everyone has a clear understanding of the tasks which are involved in the collection of data as well as the organizational practices.

This also provides an opportunity to identify deviations from best practices and document a course-correcting process which can correct these if they occur.

Educating Staff

A common hurdle found in clinical trial data management is the lack of industry-wide visibility in best practices. CROs create standard procedures of their own which makes it difficult to compare one organization’s management practices with similar organizations in the industry. As a result, there could be best practices which an organization may not have considered.

Educating staff members keeps them up-to-date on clinical trial data management processes which are practiced in the industry. It also keeps them motivated, builds confidence and raises their competency levels which lead to better results.

Using the Right Systems

In the process of developing SOPs which conform to industry best practices, it is important to look at constantly improving the quality of clinical data collection.

Collecting accurate and quality data is a key requirement of clinical trial biometrics data management and is largely dependent on the systems which are being implemented.

When managing data, the use of electronic data capture (EDC) systems should be considered to encourage staff to follow best practices. The systems should be easy-to-use and result in a reduction of potential errors when reporting.

An effective EDC system needs to be secure, minimize inaccurate data collection and allow data to be exported effectively. Some systems offer features such as edit checks,  audits, conditional forms, time point tolerances and allow medical coding language to diminish the potential for improper data entry and increase the integrity of your clinical data.

If the clinical trial involves investigational new drug (IND) testing, the EDC system should be validated and should also be compliant with 21 CFR Part 11.

In addition to all of this using the right systems also have other benefits such as meeting electronic signature requirements and robust communication with vendor systems. This elevates the value of clinical trial data for sponsors.

Steps Involved in Clinical Data Management

The following are steps involved in clinical trial data management:

Generating Source Data: This includes data such as patient diaries, medical records and laboratory results.

Transcription: In case paper Case Report Forms (CRFs) are used, clinical site records are transcribed onto these.

Data Entry: Data from the CRFs and other sources, are entered in the clinical trial database. Using
 Electronic CRFs (eCRFs) allows data entry from source documents directly to the database.

Data obtained from paper CRFs are generally entered twice and reconciled to reduce the error rate.

Data are always checked for accuracy, completeness and quality. This can require queries to be raised to the clinical site.

Locking: When data is considered final, the database is locked
Reformatting Data: For ease of analysis and reporting, listings, tables and figures are generated.

Data Analysis: Data is analyzed and reports are produced. Results are documented and additional high-level reports are produced. These include Clinical Study Reports (CSRs) and Investigator’s Brochures (IBs).

Archiving: Database and other study data are archived.

While these are the common steps which are followed, they are not necessarily in order. For example, longer studies commonly require generating intermediate discrepancies as well as listings to identify problems which need to be corrected before the study is completed.

This is a guest blog post.

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