Another predefined sheet in the template input file controls which cross-tabulations are to be generated, this is only set up one time upfront. This convention is used throughout the template. Note the use of dashes/hyphens as wildcards. In a predefined sheet in the SDTM template the user will define excludeddata=_all_ and excludedvar=-seq -REFID to exclude - SEQ and -REFID from all domains from the Valuelist summary. These selections are included in the SDTM/ADAM template in a standard, pattern-like manner to exclude from the valuelist summary. For example, there are variables for which there is no interest to see in the report, like -SEQ and -REFID. The input templates allow the user to specify which domains and variables to analyze as well as to restrict how many levels of that data to display. For example, Summar圜lass_SDTM_template.xls allows the user to identify key information such as study or studies and directory paths for comparisons. These include two similar input templates for either SDTM or ADaM. INPUT FILES To produce the summary datasets as described above, three additional Excel templates are needed to generate the Power Data Explorer dynamic Excel file. It can easily be refreshed as you receive new data. The data from inmeta.xlsx is used by the dynamic Power Data Explorer spreadsheet template that includes pivot tables and filters that allow for easy point, click and filter to get a comprehensive overview of the data. LBCAT*LBSCAT*LBTESTCD*LBTEST*LBORRESU*LBORRES These datasets are converted to an intermediate Excel spreadsheet called inmeta.xlsx. Variable level metadata including variable level attributes, number of subjects, observations Value level metadata including frequency counts per value Table of relationship values across variables, e.g. Events, Interventions, Findings, BDS, Occurrence.
Caseware idea cross tabulattion series#
SUMMARIZING THE DATA A SAS program called PDE.sas calls a series of macros that summarizes the data and produces several datasets as described below: 1Ģ SAS Dataset Description Dataset level metadata including studyid, data standard (SDTM or ADaM), datasets, labels, filesize, number of observations, number of variables, number of subjects and CDISC class, e.g. The tool is simple to set up with minimal user input, one SAS program and ability to refresh the data directly in the Excel pivot table. And it can be used for both SDTM or ADaM data. It can be also used on a single database, to compare different database releases, or even across different trials. On these standardized summaries, a set of predefined Excel pivot tables are applied with built-in filters to allow users to quickly review data. SAS is used to summarize your data in a standardized, yet flexible, manner.
Caseware idea cross tabulattion trial#
EASY DATA EXPLORATION Power Data Explorer provides a powerful, easy-to-use means of exploring and reviewing clinical trial data. This paper describes how the combination of SAS and Excel Pivot tables can provide an efficient way to review and explore data. Opening SAS data sets requires scrolling up and down, left and right over and over again. In the past this was always a tedious, repetitive and time-consuming process involving comparing/exploring define.xml or via SAS programming with hundreds of pages of outputs to review. It is also important to have that same in-depth knowledge of the ADaM data and changes in the data over time.
INTRODUCTION Clinical data analysis requires the programmer to have an in-depth knowledge of the source SDTM data to translate the statistical analysis requirements into ADaM data for analysis and reporting. This technique also compares differences between versions and can be used in clinical data analysis and can be extended to other uses. This is accomplished via a combination of SAS PROCS to summarize your data in a standardized way and Excel Pivot tables on these summary tables, to help users better understand the data including elements (data, variable, value), structure/hierarchy (cross-table) and relationship across data, and other patterns of the data. This paper explores a technique to perform an easy-to-explore overview of multiple SAS datasets in an all-in-one, dynamic report. Usually we write ad-hoc procs or data steps to summarize and compare different data, or we develop sophisticated macros that create multi- page listings or even dynamic html files to summarize all SAS datasets. 1 Power Data Explorer (PDE) - Data Exploration in an All-In-One Dynamic Report Using SAS & EXCEL ABSTRACT Harry Chen, Qian Zhao, Janssen R&D China Lisa Lyons, Janssen R&D US Getting to know your data is the first key step for every data analyst.
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