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DOMAIN 6: Data Management and Informatics

Encompasses how data are acquired and managed during a clinical trial, including source data, data entry, queries, quality control, and correction and the concept of a locked database


6.1

Describe the role and importance of statistics and informatics in clinical studies

Fundamental Level:

A1.       Understand the basic purpose of statistics and informatics as applied in clinical studies (e.g., randomization, sample size, adverse events, analysis, results)

Example: When reviewing a protocol and case report form, recognizes the data points that are associated with analysis of safety and efficacy endpoints.

Skilled Level:

B1.       Perform randomization activities to ensure accurate designation of new study participants

B2.       Describe the statistical requirements to answer the study question (hypothesis) in a study protocol

Example: Generates descriptive statistics to illustrate enrollment and safety data in a study for a staff meeting presentation.

Advanced Level:

C1.       Develop a statistical analysis and data management plan for a clinical study

Example: Develops and annotates a case report form for a clinical trial that will ensure accurate data collection in keeping with the study protocol.


6.2

Describe the origin, flow, and management of data through a clinical study

Fundamental Level:

A1.       Describe the basic concepts of clinical data management.

A2.       Identify the various sources of data that contribute to a clinical study and can distinguish the different industry standards to be used in their handling.

Example: Understands the purpose and scope, as well as the process workflow defined in a data management plan.

Skilled Level:

B1.       Apply all aspects of the clinical data management plan (CDMP) to an active clinical study with regards to the flow of data from the site to the clinical database as well as the flow of data from other sources, for example laboratory electronic uploads, EMR transfers, etc.

B2.       Manage queries and recommend whether the flow and quality of the clinical data meets the standards set in the CDMP.

Example: Performs an analysis of the data flow from various sources (e.g., Esource, third-party sources, etc.) to ensure clean data transfers per predefined specifications.

Advanced Level:

C1.       Create the clinical data management plan for a clinical study

C2.       Analyze and modify standard operating procedures, when necessary to accommodate the inclusion and implementation of new technology in the data management process or new industry-wide initiatives (e.g. data transparency and clintrials.gov requirements or the MRCT initiatives on data sharing, etc.).

C3.       Educate and mentor others concerning their role and responsibility in the conduct and management of clinical data across each aspect of the clinical research enterprise.

Example: Participates at an investigator meeting to review the clinical data management process and the responsibilities each PI and site has in the process.


Fundamental Level:

A1.       Identify and apply standard and best practices for data management in clinical research.

A2.       Identify documents and resources related standards and best practices associated with the collection, data capture, data management, data analysis, and data reporting in clinical research.

Example: When given standardized scenarios, the researcher identifies a standard or best practice (for data collection, capture, management, analysis, and reporting).

Skilled Level:

B1.       Implement industry, federal and GCP accepted standards and best practices for data management in a clinical study.

B2.       Perform data management activities across clinical studies from creation of protocol specific source documents, collection and entry of data and performing quality audits

Example: Collects and enters data into new electronic data collection forms with timeliness, accuracy and low query rates.

Advanced Level:

C1.       Develop a data management plan for a clinical study that includes standardized plans for data collection, data capture, data management, data analysis, and data reporting that use industry-accepted standards or best practices.

Example: Develops an annotated CRF for a specific study according to the data management plan for that study.

6.3

Describe best practices and resources required for standardizing data collection, capture, management, analysis, and reporting


Fundamental Level:

A1.       Identify and understand processes that assure data quality.

A2.       Recognize whether individual pieces of data collected in a clinical study are attributable, accurate, complete and verifiable from the source data.

Example: Enters and corrects data from a source document into an electronic data collection form.

Skilled Level:

B1.       Independently ensure compliance with data quality related SOPs

B2.       Provide input and share ideas, pro- and reactively, related to data quality and the related processes.

Example: Suggests a change in an eCRF design to a sponsor to help avoid recurrent queries.

Advanced Level:

C1.       Create/define data quality related SOPs or study-specific procedures for the conduct of a clinical trial.

C2.       Advise the data management team on data quality related processes that impact the clinical trial team, ensuring a smooth and constructive collaboration and communication between both.

C3.       Train trial staff on data quality related procedures and provide oversight and support in cases of doubt or risk for non-compliance.

Example: Generates an eCRF that complies with data quality standards defined by the institution or company.

6.4

Describe, develop, and implement processes for data quality assurance