Written by Bassem Toeama

From my position as a pharmacovigilance consultant at Axiom Real-Time Metrics, I acquired an in-depth perception of the different career pathways in the biotechnology/pharmaceutical industry. I will walk you through these career pathways. Today I will explain in depth the tasks and responsibilities of the diverse career levels in data management, relate each career level with the required knowledge, experience, and set of soft skills, and advise on how to progress up the career ladder.

The definition of “data management” provides an overview for the job role. As defined by DAMA (Data Management Association), data management is the development and execution of policies and procedures that organize, control, and protect the data throughout the full data lifecycle to generate high-quality, reliable data for statistical analysis. The data management professional will support the development of the investigational medicinal product in the pre-market clinical trial setting by organizing, controlling, and managing the clinical data entered by the research site in the clinical database. This will ensure retainment of high quality clinical data for statistical analysis. Hence the keywords that define the tasks and responsibilities of a data management professional include: data collection, medical coding, data validation, data reconciliation, data extraction, and communication with all involved stakeholders.

1. Data Collection

A data management professional will collect the clinical data entered in the clinical database using a web-based electronic data collection system featuring eCRF (Electronic Case Report Form). The eCRF is a customized study-specific electronic questionnaire which is designed by the product manager with a construction management software.

2. Medical Coding

A data management professional will code the collected clinical data into standard medical terminology as per the Medical Dictionary for Regulatory Activities (MedDRA) and the World Health Organization Drug Dictionary (WHO-DD), respectively, in accordance with the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use Multidisciplinary guidelines (ICH M1). The collected clinical data can include, but not limited to:

a) Medical history, social history, obstetrics history, investigations, and surgical and medical procedures by its preferred term (PT) (a code that classifies by disease diagnosis or therapeutic indication) and system organ class (SOC) (a code that classifies by etiology or manifestation site).

b) Medications, blood products, herbal remedies, homeopathic remedies, nutritional supplements, and vaccines by its anatomical-therapeutic-chemical (ATC) name (a code that classifies by the main therapeutic use) and international nonproprietary name (INN) (a code that classifies by the active pharmaceutical ingredient).

3. Data Validation and Reconciliation

A data management professional will validate the coded clinical data. The data management professional will use the following tools to resolve any errors, missing data, duplicate data, data discrepancies, or out of range values:

a) Edit and logic checks: edit and logic checks are automated tools that are implemented within the clinical database. They crosscheck the entered clinical data against the stored logic in the clinical database and if there is inconsistency or discrepancy, an error message will appear, and the entered data will not be saved until further action is taken to resolve the inconsistency or discrepancy. The stored logic is based on the data integrity plan which drives its input from the study’s clinical protocol. Too technical, right? Let me provide an example for elaboration. Let us assume that a study will be conducted on diabetic patients type 2 who are 40-60 years old. The clinical protocol is the clinical study document that describes how the study will be conducted (the objective(s), design, methodology, statistical considerations, etc.). The clinical protocol states that one of the inclusion criteria for the study participants is age 40-60 years old. The data integrity plan is the clinical study document that will describe the process for entering this information among others in the database and storing it as a logic. Now you have a stored logic in the database which states that the age group of the study participants is 40-60 years old. If the age entered in the eCRF is 70 years old, an error message will appear, and the entered data will not be saved until further action is taken.

b) Queries: If the entered clinical data passes the edit and logic checks and is saved successfully in the clinical database, yet upon data review in the database, the data management professional thinks that the stored data suffers from inconsistencies, incompleteness or any other sort of non-compliance with the regulations, then the data management professional will issue a query to the site for elaboration or correction.

Once validated, the data management professional may be asked to reconcile the clinical data between two distinct and separate databases. This is particularly important and non-negotiable for alignment of safety data and laboratory data in both distinct and separate databases.

4. Data extraction

A data management professional will extract the validated clinical data at regular intervals and at the end of the study. The clinical database is locked, prohibiting additional data entry and the validated clinical data within the clinical database is extracted with either a database management system or an advanced analytics software for the objective of quality control / quality assurance.

5. Communication

A data management professional will liaise with all possible stakeholders to ensure commitment to clinical trial data integrity. The identified stakeholders in the pre-market clinical trial setting include the sponsor, the site, and the regulator.

How can a data management professional progress up the career ladder? As an associate, a data management professional must have the advanced level of knowledge and experience in data structure with adequate clinical research skills and clinical medicine knowledge to collect, code, validate, and reconcile clinical data, and communicate with all possible stakeholders. To become an expert, a data management professional must have, in addition to the previously mentioned skills, knowledge, and experience in:

a) Quality control / quality assurance skills for the extracted clinical data.

b) Medical writing skills to write up the Data Management Plan (DMP) and Data Integrity Plan (DIP).

c) Knowledge of regulatory affairs.

In the next blog, I will explain in depth the tasks and responsibilities of the diverse career levels in medical science liaison and relate each career level with the required knowledge, experience, and set of soft skills.

Written by LSCDS Exec Member Bassem Toeama

As a former Pharmacovigilance Specialist at Axiom Real-Time Metrics, Bassem has learned the ins and outs of working at a biotechnology/pharmaceutical company. He shares his knowledge and expertise in a Blog Series on the LSCDS website.