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At PharPoint, we pride ourselves on producing quality statistical output. How are we so confident in the quality of our product? Find out in this video with Senior Manager of Statistical Programming Tracy Pflaumer and Manager of Statistical Programming Amber Urban.
Here at PharPoint research, we pride ourselves on producing quality statistical analyses.
You may be wondering how are we able to ensure the quality of our product. It all starts with strategic planning of the analysis which includes everything from how the study team for a project is selected to how programming is implemented and reviewed.
Our extensive use of independent verification and review of all statistical output followed by additional statistical review by our highly reputable statisticians helps ensure that nothing is sent prior to being thoroughly vetted by our experienced team.
As we mentioned before, it all starts with planning. When a project is awarded, our managers collaborate both within and across functional areas to identify the best leads and team members suited for the project.
Previous experience with the client, therapeutic area of the project, and even the phase of the project are just some of the considerations taken into account when the study team is chosen. Once the team has been selected, a kickoff meeting is scheduled.
This kickoff meeting is used to bring team members of contracted services together to discuss the study’s assumption, scope of work, design strategies, timelines, and any other details to ensure top quality deliverables and client satisfaction.
Once the project gets to the programming phase, our process for statistical programming ensures top quality output. One of our key components to ensuring top quality is our use of custom programming. Unlike others who may use rigid tools or macros to create outputs from a preset library of templates, our use of custom programming allows for ultimate flexibility.
The other key component to our process is independent programming. Independent programming is our preferred and most widely used method of verifying statistical output. The idea is that one programmer, who is referred to as the production programmer, creates the dataset or output based on the study documents/specifications.
Separately, another programmer, referred to as the validation programmer, is given the same documents and asked to programmatically recreate the results. That validation programmer then compares the results from the original output using an electronic comparison procedure while also visually inspecting the output. The two programmers work together to resolve any differences between the two results and once all differences are resolved, the results are considered independently verified by double programming.
As an extra level of validation, if a study is contracted to map the raw data into SDTM (which stands for Study Data Tabulation Model), then any production analysis datasets that are produced are programmed off of the SDTM datasets and the validation side of the analysis dataset is programmed from the RAW data.
This allows us to provide an extra level of QC to confirm that the data (and therefore the results) are not being altered when it is being mapped into SDTM. This same process would be used any time SDTM is used for an output as well.
This is a unique part of our process, and we feel is important to ensure the quality of the outputs and results.
Once the data is final and the statistical outputs are complete (which means they have already been independently verified), the study statistician then reviews for completeness and accuracy. The goal of the review to is to ensure that the outputs are consistent and that they make sense with the data that has been provided.
This is also used as a means to identify any potential data issues to provide back to data management and to also confirm the outputs are in line with the SAP. Most importantly, we want to ensure the quality of the outputs.
Contact a PharPoint representative today to discuss how our experienced team can help you with your statistical analyses.