This is a great question that touches on the nuances of SDTM (Study Data Tabulation Model) terminology. 1. Record Qualifiers in DM: The variables RFSTDTC , RFXSTDTC , RFENDTC , and RFXENDTC in the Demographics (DM) domain are referred to as "Record Qualifiers" because they define key aspects of the subject’s record that are critical to interpreting the entire dataset for that subject. In the context of DM, these variables provide important temporal references: RFSTDTC: Reference Start Date/Time. RFXSTDTC: Randomization Date/Time (Start). RFENDTC: Reference End Date/Time. RFXENDTC: Randomization Date/Time (End). These dates are central to defining the study period and key events such as randomization for each subject. Since the Demographics domain serves as a summary of each subject's participation in the study, these dates are essential for interpreting the entire record and are therefore termed "Record Qualifiers." 2. Timing Variables in Other Domains: On the other hand, __STDTC variables (like AESTDTC in the Adverse Events domain or LBSTDTC in the Laboratory domain) are called "Timing Variables" because they provide the timing of specific events or observations within those domains. These timing variables help determine when an event (e.g., an adverse event) occurred or when a measurement (e.g., a lab test) was taken. Unlike the DM domain, where the focus is on defining the overall participation timeline for the subject, the purpose of timing variables in other domains is to provide context for individual records or observations. They are critical for understanding when specific data points were collected but are not central to defining the subject's record as a whole. In Summary: Record Qualifiers (DM domain): Key dates that qualify the entire record for a subject, providing crucial context for interpreting the subject’s participation in the study. Timing Variables (Other domains): Dates that provide context for specific events or observations, helping to understand the timing of individual records. Understanding this distinction is important for correctly interpreting the SDTM datasets and ensuring proper data analysis. I hope this clarifies the difference! Please feel free to ask if you have further questions. Best regards, Sarath
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