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Developing the DC (Demographics as Collected) SDTM Domain Developing the DC (Demographics as Collected) SDTM Domain: Tips, Techniques, Challenges, and Best Practices Introduction The DC (Demographics as Collected) domain is a specialized SDTM (Study Data Tabulation Model) domain designed to capture demographic data as it was originally collected in clinical trials. This domain is particularly valuable in studies that involve multiple screenings or re-enrollments, where maintaining the integrity of the collected data is crucial. Unlike the DM (Demographics) domain, which provides a standardized summary of demographic data, the DC domain preserves the raw, unstandardized data, ensuring accurate data representation. This article delves into the key aspects of developing the DC domain, including the challenges, best practices, and the critical differences between the DC and DM domains. We also integrate insights from industry pa...

Efficient Directory Management in SAS: Copying Directories Mastering Directory Management in SAS: A Guide to Copying Directories In data management and processing, efficiently handling directories is crucial. Whether you're consolidating project files or reorganizing data storage, copying directories from one folder to another can streamline your workflow. In this blog post, we'll explore a powerful SAS script that automates this task, ensuring you can manage your directories with ease and precision. Objective The goal of this SAS script is to copy all directories from a source folder to a target folder. This can be particularly useful for tasks such as archiving, backup, or restructuring data storage. Below, we provide a comprehensive breakdown of the SAS code used to achieve this. SAS Code for Copying Directories %let source=/data/projects/2024/Research/Files ; %let target=/data/projects/2024/Rese...

SAS Macro for Directory Management Efficient Directory Management in SAS: A Custom Macro Managing directories effectively is crucial for organizing and handling large volumes of files in SAS. In this article, we'll walk through a custom SAS macro that helps you identify all folders within a specified directory. This macro is particularly useful for managing directory structures in complex projects. Macro Overview The get_folders macro is designed to list all folders present in a specified directory. It verifies the existence of the directory, retrieves the names of all items within it, and outputs this information in a readable format. Below is the complete SAS code for this macro: %macro get_folders(dir); /* Macro: get_folders Purpose: Identifies all folders available within a specified directory location. Source: Custom macro developed for directory management in SAS. Date: Sep...

SAS Functions: SOUNDEX, COMPGED, and Their Alternatives SAS Functions: SOUNDEX, COMPGED, and Their Alternatives Introduction In SAS, the SOUNDEX and COMPGED functions are powerful tools for text comparison, particularly when dealing with names or textual data that may have variations. In addition to these, SAS offers other functions like DIFFERENCE and SPEDIS that provide additional ways to measure similarity and distance between strings. This article explores these functions, provides examples, and compares their uses. The SOUNDEX Function The SOUNDEX function converts a character string into a phonetic code. This helps in matching names that sound similar but may be spelled differently. The function generates a four-character code based on pronunciation. Syntax SOUNDEX(string) Where string is the character s...

Using SUPPQUAL for Specifying Natural Key Variables in Define.XML Using SUPPQUAL for Specifying Natural Key Variables in Define.XML Author: Sarath Introduction Define.XML plays a critical role in specifying dataset metadata, particularly in the context of clinical trial data. One important aspect of define.xml is the identification of natural keys, which ensure the uniqueness of records and define the sort order for datasets. Using SUPPQUAL for Natural Keys SUPPQUAL, or Supplemental Qualifiers, is a structure used in SDTM/SEND datasets to capture additional attributes related to study data that are not part of the standard domains. In certain cases, the standard SDTM/SEND variables may not be sufficient to fully describe the structure of collected study data. In these cases, SUPPQUAL variables can be utilized as part of the natural key to ensure complete and accurate dataset representation. Example Scenarios Consider ...

Optimizing Data Processing with Multi-Threaded Processing in SAS Optimizing Data Processing with Multi-Threaded Processing in SAS Author: Sarath Date: August 31, 2024 Introduction Multi-threaded processing in SAS leverages the parallel processing capabilities of modern CPUs to optimize data handling and analytical tasks. This approach is particularly beneficial when working with large datasets or performing computationally intensive operations. By distributing the workload across multiple threads, SAS can process data more efficiently, leading to reduced runtime and better utilization of available resources. Why Use Multi-Threaded Processing? As datasets grow in size and complexity, traditional single-threaded processing can become a bottleneck, leading to longer runtimes and inefficient resource utilization. Multi-threaded processing addresses these issues by: Distributing tasks a...

Comparing and Contrasting SAS Arrays, PROC TRANSPOSE, and Other SAS Techniques: A Detailed Guide with Examples In SAS programming, handling and transforming data can be accomplished using various techniques, including SAS arrays, `PROC TRANSPOSE`, and other methods such as `DATA` steps, `PROC SQL`, and `MACROs`. This report provides a comprehensive comparison of these techniques, highlighting their use cases, advantages, limitations, and best practices. Detailed examples with SAS code are included to illustrate each approach. 1. Overview of SAS Arrays SAS arrays provide a powerful way to perform repetitive operations on multiple variables within a `DATA` step. Arrays allow you to process a group of variables as a single entity, making it easier to apply the same operation to multiple variables without writing repetitive code. 1.1. Use Cases for SAS Arrays Applying the same calculation or transformation to multiple variables. Reformatting data from wide to long format ...