Quick Tip: See SAS Dataset Labels Without Opening the Data Quick Tip: See SAS Dataset Labels Without Opening the Data When working with SAS datasets, checking the dataset label without actually opening the data can be very useful. Whether you're debugging or documenting, this small trick can save you time and effort! 1. Use PROC CONTENTS PROC CONTENTS is the most common and straightforward way to view the dataset label. proc contents data=yourlib.yourdataset; run; The dataset label will appear in the output as the field: Data Set Label . 2. Query DICTIONARY.TABLES or SASHELP.VTABLE For a programmatic approach, use the DICTIONARY.TABLES table or SASHELP.VTABLE view to query dataset metadata. Example Using PROC SQL proc sql; select memname, memlabel from dictionary.tables where libname='YOURLIB' and memname='YOURDATASET'; quit; ...

Detecting Non-Printable Characters in SDTM Datasets Using SAS Non-printable characters in datasets can lead to errors and inconsistencies, especially in the highly regulated environment of clinical trials. This blog post demonstrates how to create a SAS program that identifies non-printable characters in all SDTM datasets within a library and generates a comprehensive report. Why Detect Non-Printable Characters? Non-printable characters, such as ASCII values below 32 or above 126, can cause issues during data validation, regulatory submissions, and downstream processing. Detecting them early ensures the quality and compliance of your SDTM datasets. The SAS Program The following SAS program processes all SDTM datasets in a library and generates a combined report of non-printable characters, including: Dataset name : The dataset where the issue exists. V...

SDTM aCRF Annotation Checklist SDTM aCRF Annotation Checklist By Sarath Annapareddy Introduction Creating an SDTM Annotated Case Report Form (aCRF) is a critical step in clinical trial data submission. It ensures that data collected in the CRF maps correctly to SDTM domains, adhering to regulatory and CDISC standards. This checklist serves as a guide to creating a high-quality SDTM aCRF ready for regulatory submission. 1. General Formatting Ensure the aCRF uses the latest SDTM IG version relevant to the study. The document should be clean, legible, and free of overlapping annotations. Page numbers in the aCRF should align with the actual CRF pages. Annotations must be in English, clear, and consistently for...

Common P21 SDTM Compliance Issues and How to Resolve Them Common P21 SDTM Compliance Issues and How to Resolve Them By Sarath Annapareddy Introduction Pinnacle 21 (P21) is a cornerstone for validating SDTM datasets against CDISC standards. Its checks ensure compliance with regulatory requirements set by the FDA, PMDA, and other authorities. However, resolving the issues flagged by P21 can be challenging, especially for beginners. This post dives into common P21 compliance issues and provides actionable solutions with examples. 1. Missing or Invalid Controlled Terminology Issue: P21 flags variables like LBTESTCD or SEX as non-compliant with CDISC Controlled Terminology (CT). This happens when values in your datasets are o...

Define.xml and cSDRG QC Checklist for FDA and PMDA Submissions Comprehensive QC Checklist for Define.xml and cSDRG Ensuring Quality and Compliance for FDA and PMDA SDTM Submissions Introduction The Define.xml and Clinical Study Data Reviewer’s Guide (cSDRG) are critical components of SDTM submissions to regulatory agencies like the FDA and PMDA. These documents help reviewers understand the structure, content, and traceability of the datasets submitted. A robust QC process ensures compliance with agency requirements, minimizes errors, and enhances submission success. This blog outlines a detailed manual QC checklist for both Define.xml and cSDRG, emphasizing key differences between FDA and PMDA requirements. Define.xml QC Checklist 1. Metadata Verification Verify all datas...

FDA vs PMDA Data Quality Checks Differences in Data Quality Checks Between FDA and PMDA Introduction Submitting SDTM datasets to regulatory authorities like the FDA (U.S. Food and Drug Administration) and PMDA (Japan's Pharmaceuticals and Medical Devices Agency) involves rigorous data quality checks. While both agencies adhere to CDISC standards, their submission guidelines and expectations differ in certain aspects. This blog explores the key differences in data quality checks for FDA and PMDA submissions. Similarities in Data Quality Checks Both FDA and PMDA share several common expectations for SDTM datasets: Adherence to CDISC Standards: Both agencies require compliance with the SDTM Implementation Guide (SDTM-IG). Controlled Terminology (CT): Variables such as AEDECOD and LBTESTCD must align with CDISC CT. Traceability: Ensures that derived datasets and analysis results can be traced back to ...

Advanced SDTM Programming Tips Advanced SDTM Programming Tips Streamline Your SDTM Development with Expert Techniques Tip 1: Automating SUPPQUAL Domain Creation The SUPPQUAL (Supplemental Qualifiers) domain can be automated using SAS macros to handle additional variables in a systematic way. Refer to the macro example provided earlier to simplify your SUPPQUAL generation process. Tip 2: Handling Date Imputation Many SDTM domains require complete dates, but raw data often contains partial or missing dates. Use the following code snippet for date imputation: data imputed_dates; set raw_data; /* Impute missing day to the first day of the month */ if length(strip(date)) = 7 then date = cats(date, '-01'); /* Impute missing...

Advanced SDTM Programming Tip: Automating SUPPQUAL Domain Creation Advanced SDTM Programming Tip: Automating SUPPQUAL Domain Creation Optimize Your SDTM Workflows with Efficient Automation Techniques Introduction to SUPPQUAL Automation The SUPPQUAL (Supplemental Qualifiers) domain is used to store additional information that cannot fit within a standard SDTM domain. Manually creating the SUPPQUAL domain can be time-consuming and error-prone, especially for large datasets. In this article, we’ll explore an advanced tip to automate its creation using SAS macros. Use Case: Adding Supplemental Qualifiers to a Domain Imagine you have an SDTM AE domain (Adverse Events) and need to capture additional details like the investigator’s comments or assessment methods that are not part of the stan...

Advanced SAS Programming Tip: Using HASH Objects Advanced SAS Programming Tip: Using HASH Objects Unlock the Power of SAS for Efficient Data Manipulation Introduction to HASH Objects In SAS, HASH objects provide an efficient way to perform in-memory data lookups and merge operations, especially when dealing with large datasets. Unlike traditional joins using PROC SQL or the MERGE statement, HASH objects can significantly reduce computational overhead. Use Case: Matching and Merging Large Datasets Suppose you have two datasets: a master dataset containing millions of records and a lookup dataset with unique key-value pairs. The goal is to merge these datasets without compromising performance. Code Example: Using HASH...

Advanced SAS Programming Tip: Mastering Macro Variables Advanced SAS Programming Tip: Mastering Macro Variables Unleash the power of SAS with this advanced technique. Introduction Macro variables are a powerful tool in SAS that allow you to dynamically generate code. By understanding and effectively using macro variables, you can write more efficient and flexible SAS programs. The Basics of Macro Variables A macro variable is a placeholder that is replaced with its value during macro processing. You define a macro variable using the %LET statement and reference it using the %SYSFUNC or %SYSEVALF functions. Advanced Techniques 1. Conditional Logic You can use the %IF-%THEN-%ELSE statements to create conditional logic within your macro code. This allows you to dynamically generate code based on specific conditions. 2...