SAS and Proc Report in Clinical Data Science

SAS (Statistical Analysis System) is a widely used professional analytics software for data analysis, statistical modeling, and data mining, with extensive applications in clinical data science. Proc Report, a powerful report generation procedure in SAS, offers robust support for producing clinical reports. Unlike the more basic Proc Print, which is mainly used for simply displaying dataset content and offers limited support for customization, grouping, and conditional calculations, Proc Report is highly customizable and flexible. It allows users to control the layout, style, and content of reports in detail—meeting the complex and variable needs of clinical programming and aligning with regulatory requirements for clinical research.

Key Applications of Proc Report

Proc Report is widely used in various aspects of report generation, including:

· Report output customization

· Title and style formatting

· Generation of publication-quality “three-line tables”

· Grouped and summarized reports

· Custom calculations and complex table structures

· Conditional formatting and highlighting

· Pagination and column control

· Dynamic report generation and parameterization

Output Formats

HTML Output

HTML output is ideal for viewing and sharing reports on the web and supports interactive elements like hyperlinks and buttons. To output Proc Report results in HTML format, users can utilize ODS HTML (Output Delivery System). ODS controls the output format, file path, and styling. HTML format enhances interactivity and user experience.

PDF Output

PDF format ensures layout consistency across platforms and supports embedding charts and images. To output Proc Report results in PDF format, use the ODS PDF statement. It allows for visual enhancements by embedding graphical outputs (e.g., generated via proc sgplot).

RTF Output

RTF (Rich Text Format) is one of the most common output formats for Proc Report. It supports rich text formatting (fonts, colors, paragraph styles). To generate RTF output, use the ODS RTF statement.

Performance Optimization and Output Efficiency

To improve the performance of PROC REPORT, preprocessing the data before passing it to PROC REPORT can significantly reduce data volume and enhance report generation speed. Simplifying the computation and formatting logic in PROC REPORT code can also improve report generation efficiency. Avoid complex calculations in COMPUTE blocks and prefer using simple DEFINE statements instead. Additionally, using indexes can speed up data access, while views can reduce physical storage requirements and further optimize report generation.

Techniques for Handling Large Datasets:

1. Data Sampling Randomly selecting a subset of data for reporting can significantly reduce processing time.

2. Chunk Processing Splitting large datasets into smaller blocks, processing them separately, and then combining the results improves manageability and speed.

3. Distributed and Parallel Computing SAS supports distributed and parallel processing (e.g., through SAS Viya) to significantly enhance efficiency for large datasets.

By preprocessing data, optimizing report logic, using indexes/views, and applying parallel/distributed processing, report generation becomes significantly faster and more stable.

Advanced Tips for Proc Report

Advanced features in Proc Report can enhance readability and usability, especially for complex reporting needs.

Conditional Formatting and Highlighting

In clinical studies, it's often necessary to highlight key data based on specific conditions. Proc Report provides multiple methods to implement conditional formatting, with the most commonly used approach being the CALL DEFINE function within a COMPUTE block.2.

Pagination and Column Control

For long reports, proper pagination and column layout improve readability. The BREAK statement is useful for inserting group headers, summary rows, or page breaks.

Dynamic Report Generation and Parameterization

In multi-project or repetitive reporting tasks, dynamic parameterization greatly improves coding efficiency. Macro variables can be used to dynamically set data sources, variable lists, titles, and more.

These techniques allow Proc Report to produce professional, flexible, and practical reports. When applied appropriately, they provide strong support for clinical research by efficiently generating high-quality outputs.

Asymchem Clinical (Clin-nov) Department of Data Management and Statistical Programming

The Statistical Programming Department focuses on providing compliant, professional, timely, and internationally standardized data management and statistical programming services. The team is composed of core talents from global pharmaceutical companies and CROs.

· A stable, experienced team of over 120 professionals

· Supporting more than 100 domestic and international innovative pharmaceutical companies

· Successfully implemented over 500 statistical programming projects

· The first CDISC corporate member in China, with deep expertise in CDISC standards and international-standard operations.