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1. Can you tell us about your experience in clinical data management?
Answer: Yes, I have X years of experience in clinical data management, including data collection, cleaning, and quality control for clinical trials.
2. Can you describe your knowledge of clinical data standards and regulations?
Answer: I am familiar with industry standards such as CDISC, and regulations such as 21 CFR Part 11 and the EU Clinical Trials Regulation, and ensure that data management practices comply with these standards and regulations.
3. How do you approach data cleaning and quality control in a clinical trial?
Answer: I follow a systematic approach to data cleaning, including identifying and resolving outliers, missing data, and inconsistent values, and performing data validations and cross-checks to ensure data quality.
4. Can you give an example of a complex clinical data management project you have worked on?
Answer: I worked on [project name], where I managed [data type] for [trial phase] of a [therapeutic area] study. I utilized [tools/methods] and collaborated with [interdisciplinary team] to ensure accurate and efficient data management.
5. How do you ensure the accuracy and integrity of clinical trial data?
Answer: I follow established data management processes and procedures, perform regular audits and quality checks, and utilize secure data storage and backup systems to protect data integrity.
6. How do you stay updated with the latest developments in clinical data management?
Answer: I attend relevant conferences and workshops, follow industry experts and thought leaders, and engage in ongoing learning and professional development opportunities.
7. How do you approach ensuring data quality in clinical trials?
Answer: I follow established data management plans and standards, perform data validation and reconciliation, and continuously monitor data to identify and resolve any discrepancies.
8. How do you handle missing or inconsistent data in clinical trials?
Answer: I work with study teams to identify the root cause of missing or inconsistent data and implement appropriate corrective actions, such as queries or data reconciliation.
Fri, 16 Jun 2023
Fri, 16 Jun 2023
Fri, 16 Jun 2023
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