A clinical research organization is using predictive analytics to improve patient safety and decrease costs...

Data validity refers to the extent to which data accurately represents the phenomenon or concept that it is intended to measure1. Data validity is essential for predictive analytics, as it affects the quality and credibility of the analysis results and the subsequent decisions or actions based on them. If data is invalid, the predictions may be inaccurate, misleading, or irrelevant. However, data validity may be challenging to ensure in clinical research, as data may be scarce, incomplete, inconsistent, or subject to errors or biases2. Therefore, the team may establish scenario development as a required technique to address this concern. Scenario development is a form of document analysis that involves creating hypothetical situations or stories based on assumptions, evidence, and logic to explore the possible outcomes or implications of a problem or opportunity3. Scenario development can help the team generate appropriate data for initial analysis by simulating different conditions, variables, or events that may affect the clinical trials, and by testing the validity of the data against the scenarios4.

References:1: Validity in data collection methods - OpenLearn - Open University, 2: Data Quality in Clinical Research - NCBI - NIH, 3: Scenario Analysis: How It Works and Examples - Investopedia, 4: Predictive Analytics using simulation models - AnyLogic