Introduction
Are you a clinical SAS professional and looking at adding real value to your portfolio or resume? Are you thirsty for knowledge and engaging yourself in learning new things along the way? Then we suggest you the list of Top 5 Clinical SAS Project Ideas that you can consider enriching your professional experience.
The major learning part of Clinical Data management comes from actual “doing” the data management projects rather than from the print. Human brain is wired to learn things by observing and experiencing. When you start doing the projects. It helps in actually understanding the problem and applying your knowledge and skills in the field of clinical Trial Data management. This helps in greater retention, deeper understanding of clinical Data management.
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Clinical Data analysis with Python and SAS:
In clinical trial data management, SAS is the most powerful tool in analyzing the data, producing figures and listings as part of clinical Study reports in the drug development process. It has been a fully validated standard software in data handling, analyzing and visualization in reporting the statistical analysis reports in CTMS Clinical Trial data management.
Python, on the other hand, is a free software with tremendous functionalities. Python provides useful modules to access and handle Clinical SAS Programming datasets via SASPy bodules (NAKAjima 2018). Users can also access pandas and NUMPy, which are python’s powerful libraries, while on this project.
Benefits:
- This Clinical SAS project idea will enhance your data handling skills. You will gain deeper insights into patient demographic and treatment outcomes
- Also, this clinical SAS project idea polishes your skills in data cleaning and preparation using Python.
- Using Clinical SAS programming for couplex clinical trials and CTMS Clinical Trial data analysis.
- You will learn real world applications of Clinical SAS along with integration of Python to streamline CTS clinical trial data.
Read Our Blog Post: “Patients Retention Strategies: Boost Clinical Success”
Automating Adverse Event Reporting with RPA and clinical SAS
Credit: Aayan Ansari (Graphic Designer)
Currently, in any pharmaceutical companies, adverse event reporting in clinical trial data management and CTs clinical trials are done manually by a designated group based on a clinical data management report. The annual reporting process is time consuming and error-prone because of constant back and forth steps and a lot of reviews among the different groups.
This tedious process therefore needs an innovative and automated process that can streamline the adverse event reporting. The Robotic Process Automation RPA bot is developed to improve accuracy, efficiency, reduce manual entry errors and consistency in the reporting. The RPA bot can gather data from different clinical trial and CTMS Clinical Trial data management sources and feed it to Clinical SAS for further reporting.
Benefits:
This clinical SAS project Idea is best suited for proactively streamline the result posting process as it is accurate, time saving and can handle the clinical trial data effectively.
As a clinical SAS professional, you will understand the basics of RPA automation and data collection from CTS clinical trials.
Data visualization for Clinical trials with Tableau and SAS:
In clinical trials, the clinical trial data management is used to analyse and monitor a lot of parameters including, research site characteristics and performances, patient enrollment, safety and follow up. Presenting and understanding this enormous amount of data can be difficult.
Data visualization can be used to represent the same data in an interactive way. It also saves time with no decrease in accuracy compared to traditional static reporting. While preparing operational reports, any CROs use SAS Visual Analytics geo aps to monitor the data. The reports can have pie charts, bar charts,line charts, dropdown venus and detailed listing tables to showcase the parameters like patient status from screening to the end, subset enrollment, site reports.
You can use Clinical SAS visual analytics to gather and process the data and conduct a statistical analysis. Then by using Tableau, you can create interactive, easy to understand dashboards to patient demographics, treatment results and various adverse event reporting.
Benefits:
The main objective of this Clinical SAS project idea is to help students create interactive yet accurate clinical trial data management reports, adverse event reports.
The complex CTS clinical trial data can be represented in easy to understand and tie saving reports.
Students can learn and create these dynamic, effective and engaging dashboards using Tableau.
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Building CDISC complaint datasets using SQL and Clinical SAS:
Credit: Aayan Ansari (Graphic Designer)
Clinical Data management demands accuracy and efficiency. It also should be compliant with the regulatory bodies. Now generating reports that are adopted to CDISc guidelines within the organizations or clinical research program, are far easier as the Congress approved the FDA safety and Innovation Act in July 2012. The organizations have seen clear advantages in adopting reports to CDISC standards. When data needs to be reported for regulatory submission, a substantial amount of time was spent on deciding the structure and variable names to be integrated in the reports.
Now with this clinical SAS project idea, students can extract the clinical trial data from Clinical trial data management or clinical data management or CTS clinical trial database using SQL.
Benefits:
This unique hands-on clinical SAS project idea enables students to transform clinical trial data into CDISc compliant report forms.
Students can also learn how to handle SQL. In this process, one can gain practical knowledge in extracting and managing data using SQL.
The valuable skill of transforming the raw clinical trial data into CDISC complaint report adhering to the regulatory guidelines.
A student can gain an in-depth understanding of CDISC and clinical data standards to comply with regulatory submission.
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Survival Analysis using machine learning and SAS
Credit: Aayan Ansari (Graphic Designer)
Reporting Survival analysis in a clinical trial is complex but by incorporating tie to event information, survival analysis can be used as a powerful tool than just to examine whether the end point has occurred. Survival analysis uses a set of statistical methods for exacting the event occurrence and also the timing of events. By using Clinical SAS programming any of the challenges that are frequently encountered during the time-event data can be overcome.
In this Clinical SAS project idea, students can generate an initial survival analysis report. Then using Python’s machine learning libraries to predict patient outcomes.
Benefits:
This clinical SAS project idea is an excellent opportunity for those who are interested in advancing their clinical research study.
A student can get well versed with complex survival analysis study using clinical SAS programming to learn about patient outcomes.
This clinical SAS project idea is a perfect blend in integrating statistical and predictive analysis for deeper understanding.
Conclusion
These Top 5 clinical SAS project ideas are the gateways to showcase your skills and expertise into real world clinical trial data management. With the help of these practical applications, you can find innovative solutions in analyzing patient demographics, evaluate treatment effectiveness and can create interactive and accurate visualizations for clinical trial data outcomes.
We have listed the best clinical SAS project ideas that can give you practical hands-on experience with data extraction and manipulation, statistical analysis and reporting. A skillful blend of deeper understanding of clinical trial data management and practical applications can help you secure your spot in the healthcare industry. To learn more about Clinical SAS and how you can accelerate in your career with these courses please visit our website. Enroll today for a better future!
Written by Dr. Anindita Talukdar
Previously a research associate & scholar with 9+ years of experience in academic research writing, editing, and proofreading.
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