Data Cleaning Strategies

 Three types of data errors commonly occur. Data can be incomplete, incorrect, or inconsistent. Detecting and correcting errors is an ongoing requirement in any clinical trial. Efficacy and safety data must be available for review virtually at any moment, so data cleaning is not a task that can wait until all the data are collected. To prepare, review this week’s Learning Resources regarding common data errors and procedures that can be used to detect and correct them on an ongoing basis. Post a comprehensive response to the following: • In devising a strategy for detecting errors on a CRF, what are the benefits of strict detection parameters (high sensitivity) versus more relaxed parameters (high specificity)? • How would the type of variable influence your decision? • How would the error detection strategies differ between paper and electronic CRFs?

#Data #Cleaning #Strategies

Share This Post


Order a Similar Paper and get 15% Discount on your First Order

Related Questions