Varaprasad Ilapogu, masaki mihaila, Janet Li and Ernesto Gutierrez
Datasets and statistical outputs produced by clinical SAS programming teams in the pharmaceutical industry are often validated individually by parallel programming prior to submission to other teams (e.g. Statistics and Medical Writing). This process leaves out the cross-checking of outputs against other outputs that may have similar information presented. For example, the population size of each treatment group in a study is presented in many of the outputs, yet there is often not a programmatic process in place to check whether the population sizes match across the different statistical outputs. We have developed a python script that addresses checking information across statistical outputs. The script extracts commonalities from each statistical output (e.g. individual tables and figures in RTF form) and presents the relevant information in a single, easily accessible document (e.g. an excel spreadsheet) to help facilitate the cross-checking of this information. We hope that this additional process can help enhance the quality and increase the efficiency of the study package review process.
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