PharmaSUG 2018 - AD16 - An Efficient Tool for Clinical Data Check

Sep 12, 2026

Upload by Riley

High-quality clinical trial data underpins compliance with Good Clinical Practice (GCP) and regulatory expectations. When statistical analysis uncovers inconsistencies, study interpretation can be affected, making validity checks and cross-dataset issue detection essential during analysis and reporting. This paper presents a tracking-focused approach that documents findings accurately throughout programming and statistical workflows. A macro using two sub-macros outputs an Excel workbook, compares current results with prior versions, and highlights changes in distinct colors for rapid reviewer identification and real-time issue status documentation.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages8

Reading Time3 min

AccessFree to download

Data Check Procedures

Data check specification and SAS inputs

Macro output and version comparison

Data Check Items

Discrepancies of randomized subjects

Discrepancies of death subjects

Discrepancies of administered drug

Discrepancies between administered drug and disposition datasets

PharmaSUG 2018-Paper AD-16 An Efficient Tool for Clinical Data Check Chao Su, Merck & Co., Inc., Rahway, NJ Shunbing Zhao, Merck & Co., Inc., Rahway, NJ Cynthia He, Merck & Co., Inc., Rahway, NJ ABSTRACT High-quality data in clinical trials is essential for compliance with Good Clinical Practice (GCP) and regulatory requirements. If data issues are observed during statistical analysis, the interpretation of study results is impacted. As such, statistical programmers may be asked to perform data validity checks and detect potential data issues across multiple datasets during the analysis and reporting (A&R) processes. The focus of this paper is NOT on finding data issues prospectively, but on how to accurately and efficiently track findings during the course of statistical analysis and programming processes. In this paper, some routinely performed data checks are discussed briefly. The results of the data checks are generated and summarized through a macro consisting of two sub-macros. The macro combines the results into one Excel file and compares the current check results with the ones in the previous version automatically. The changes between two versions are marked in different colors so that the reviewers can identify pending issues quickly. This tool provides real time documentation that can track data issues related to the A&R process accurately and efficiently. INTRODUCTION In the pharmaceutical industry, statistical analysis and reports are based on clinical data which must be of high quality. During the trial, the data management (DM) team applies many edit checks to clean the data. In practice, most of the checks are univariable and within the same domain. As a result, discrepancies across multiple variables and mismatches among different datasets might exist after edit checks are complete with DM tools. Additional data issues may be identified using SAS programs for analyses and reporting. Besides identifying data issues, reporting and tracking data issues are also critical. Both the clinical team and the statisticians need to review the data issue report and include comments and feedback before thereport with issue findings from SAS programs is sent to the DM. The DM will correct or query the issues according to the report. Excel is commonly used among professionals and has the flexibility required to integrate different comments into the tracking report. Therefore, it is used here as a tool to bring all the addressed data issues and feedback together. All the data issues captured by SAS programs from different sources are integrated into a single Excel workbook with multiple worksheets. Although Excel is flexible and easy to use, one needs to put a great deal of time-consuming manual effort on adjustments like column formatting to produce a tracking report with a user friendly interface. , The level of effort is incompatible with frequent reports. A macro developed to automatically output the tracking spreadsheet with a customized interface is necessary. Historical information like comments from reviewers and the date on which the issue was found are kept in the report. Updates of issue contents are highlighted with different colors so that it is very easy for the reviewer to track activities among different roles and determine issue status. DATA CHECK PROCEDURES The purpose of the data check here is to record and track data issues found during statistical analysis and reporting. It works as a supplement instead of a replacement to the data edit checks done by DM. The first step of the data check is to create the data check specification. The programmers will consult with statisticians and the clinical team to draft the data checks specification for a given project. DM will review the data check specification to avoid duplicate work by the DM edit check tool. After the specification is ready, the study programmers will follow the specification to identify issues. Different SAS datasets are

FAQs

What problem does the paper address in clinical data handling?

The paper addresses how to accurately and efficiently track and document data issues found during statistical analysis and reporting, especially when these issues impact interpretation and require reviewer and DM follow-up.

How does the proposed tool generate the tracking report?

It uses a macro composed of two sub-macros to combine data check results into a single Excel file, including one worksheet per issue category and a status summary worksheet.

How are changes between report versions handled for reviewers?

If a previous spreadsheet exists, the macro compares the new and old versions, carries forward comments when issues persist, and marks updates with different colors so reviewers can quickly identify pending or new items.

Sep 12, 2026

Upload by Riley