Course Content
Data Entry, Validation, and Access Control
This chapter explores the operational aspects of data collection within REDCap, including data entry workflows, validation procedures, user rights management, audit trails, and quality assurance activities that help ensure research data remain reliable and compliant with regulatory standards.
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Data Quality Management and Query Resolution
This topic is about ensuring that data is accurate, complete, and consistent. It covers identifying, investigating, and correcting errors or discrepancies in data. It also focuses on timely resolution of data queries to maintain data integrity and reliability.
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Introduction to R for Clinical Data Management.
This topic introduces R, an open-source programming language used for statistical analysis, data management, and visualization. In clinical research, R is applied to clean, analyze, and report data in a reproducible and reliable manner.
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Clinical Research Data Management Course

Risk-based monitoring is an approach that focuses monitoring resources on the data, processes, sites, and risks that matter most to participant safety and study reliability. Rather than checking every data point with equal intensity, risk-based monitoring asks: Which errors would most threaten the study? Which processes are critical? Which sites show concerning patterns?
Which variables affect primary outcomes, safety, eligibility, or regulatory reporting?
Risk-based monitoring aligns with modern Good Clinical Practice expectations and regulatory guidance emphasizing quality management. It does not mean less monitoring in a careless sense. It means more thoughtful monitoring. A study may reduce low-value checking of non-critical fields while increasing attention to informed consent, eligibility, primary outcomes, serious adverse events, data integrity, and site performance indicators.
The process begins with risk assessment. The team identifies critical data and processes, potential failure modes, likelihood, impact, and detectability. For example, failure to capture serious adverse events may have high impact. Incorrect coding of a secondary exploratory variable may have lower impact. Missing primary outcomes may threaten the study conclusion. Shared user accounts may threaten auditability. Each risk should have controls, such as training, validation rules, reports, central review, or targeted source verification.
Risk-based monitoring may combine central monitoring, targeted on-site visits, remote source review where permitted, and statistical checks. If central data show that a site has high query rates, delayed entry, or unusual outcome distributions, the monitoring plan may focus additional attention there. If another site has stable quality metrics, monitoring may remain routine.
Risk-based approaches require documentation. The study should have a monitoring plan explaining the rationale for risk priorities, indicators, thresholds, review frequency, and actions. Decisions should be traceable. If a critical risk is identified, the response should be documented. This makes the approach defensible during audits and inspections.