.

Advanced SDTM Programming Techniques for SAS Programmers Advanced SDTM Programming Techniques for SAS Programmers As an experienced SAS programmer working with the Study Data Tabulation Model (SDTM), it's crucial to stay updated with the latest programming techniques. Whether you're tasked with building SDTM domains from scratch or optimizing existing code, there are several advanced concepts that can improve your workflows and output. In this post, we’ll explore some techniques that can help you overcome common SDTM challenges and boost efficiency in handling clinical trial data. 1. Efficient Handling of Large Datasets When dealing with large clinical datasets, speed and efficiency are key. One method to optimize SDTM domain generation is to reduce the data footprint by eliminating unnecessary variables and duplicative observations. Consider the following approaches: Removing Duplicate ...

Excluding Variables in PROC COMPARE Using the DROP Dataset Option Excluding Variables in PROC COMPARE Using the DROP Dataset Option When comparing two datasets using PROC COMPARE in SAS, there may be cases where you want to exclude specific variables from the comparison. One efficient way to do this is by using the DROP dataset option. This option allows you to remove certain variables from consideration during the comparison process. Using the DROP Dataset Option in PROC COMPARE The DROP dataset option is applied directly to the dataset references in the BASE and COMPARE options of PROC COMPARE . When you use DROP , the specified variables are excluded from the comparison. Here is the syntax for using the DROP option: proc compare base=dataset1(drop=var_to_exclude) compare=dataset2(drop=var_to_exclude); run; In this example, var_to_exclude is the name of the variable you want to ex...

Mastering PROC SQL: Best Practices for Seamlessly Adding Columns Mastering PROC SQL: Best Practices for Seamlessly Adding Columns PROC SQL in SAS is a powerful tool, especially when you need to add new columns to existing tables. Whether you're updating tables with new data or making calculated columns, following best practices ensures your code is efficient, maintainable, and easy to understand. 1. Use ALTER TABLE to Add Columns The most straightforward way to add a column to a table is by using the ALTER TABLE statement. It allows you to add one or more columns without disrupting the existing data structure. proc sql; alter table your_table_name add new_column1 num format=8. label='New Column 1', new_column2 char(20) label='New Column 2'; quit; Tip: Always specify the data type ( num for numeric, char(n) for character) and add useful formats and labels to keep your columns clear and organized. 2. Av...

How to Address PROC COMPARE Reporting Same Values as Different in SAS How to Address PROC COMPARE Reporting Same Values as Different in SAS Working with large datasets in SAS often requires comparing data between two tables. The PROC COMPARE procedure is an essential tool for this task, but sometimes it reports values as different even when they appear to be identical. This issue can arise from various causes, such as numeric precision differences, rounding issues, or formatting inconsistencies. In this post, we will explore common causes of this issue and how to resolve them. 1. Numeric Precision Issues SAS stores numeric values using floating-point precision, which can lead to small differences that aren't immediately visible. These differences may cause PROC COMPARE to report discrepancies even though the values seem the same. Solution: Use the CRITERION or FUZZ option to define an acceptable tolerance for differences. pro...

5 Approaches to Identify Blank Columns in SAS Datasets 5 Approaches to Identify Blank Columns in SAS Datasets Author: Sarath Date: September 5, 2024 When working with large datasets in SAS, it’s crucial to ensure the integrity of your data. One common task is identifying columns that are completely blank, meaning they contain no non-missing values. In this article, we'll explore five different methods to efficiently find blank columns in SAS datasets, along with examples for each approach. 1. Using PROC MEANS or PROC SUMMARY The simplest way to identify blank columns is by using PROC MEANS or PROC SUMMARY . These procedures provide a summary of missing values for both numeric and character variables. You can quickly determine which columns are fully blank by checking the output for variables with 100% missing values. proc means data=your_dataset nmiss; var _numeric_; /* For numeric variables */ run; proc me...

Effortlessly Upcase All Variables in SAS Using PROC DATASETS Effortlessly Upcase All Variables in SAS Using PROC DATASETS When working with SAS datasets, ensuring consistency across variables, especially character variables, can be crucial. A common requirement is to upcase all character variables, converting their values to uppercase. While several methods exist to achieve this, one of the most efficient and dynamic approaches involves using the PROC DATASETS procedure. In this article, we will delve into how PROC DATASETS works and how you can use it to upcase all character variables in your dataset with minimal effort. Understanding PROC DATASETS The PROC DATASETS procedure is primarily used for managing SAS datasets within a library. It allows you to rename, delete, append, modify, and more, without the need to read or copy the data into the Program Data Vector (PDV). This makes it highly efficient, especially when you need to modify...

How to Upcase All Variables in a SAS Dataset How to Upcase All Variables in a SAS Dataset When working with character data in SAS, you may often need to ensure that all text variables are in uppercase. Fortunately, SAS provides several methods to upcase variables in a dataset, depending on your specific needs. In this article, we will explore various ways to achieve this task and provide code snippets for each method. 1. Using DATA Step with UPCASE Function The UPCASE function is the simplest way to convert all character variables to uppercase using a DATA step. data upcase_dataset; set original_dataset; array char_vars _character_; do i = 1 to dim(char_vars); char_vars[i] = upcase(char_vars[i]); end; drop i; run; This approach loops through all character variables and applies the UPCASE function to each of them. 2. Using PROC SQL with CASE Statement In this method, PROC SQL is used...