Element Technologies Inc’s Post

Early in my career, I was INCREDIBLY fortunate to work for a company that required biostatisticians to program SDTM and ADaM datasets. We did not just review these datasets and I am so thankful for that experience because now when I review final outputs, I can very quickly trace the variables involved back through the TLF program, into the ADaM, into the SDTM, and finally to the RAW. Along that journey of tracing, I can understand the derivations and transformations applied to the data enabling me to spot potential errors and trouble points.  This greatly decreases the amount of time I need to review outputs and at the same time greatly increases the quality of my final outputs as I am now catching more errors that may have gone undetected. Win-Win Put another way the RAW data does not go into some opaque box where I have no idea what is being done to it. I can have complete trust and faith in how the data was handled between the RAW and final TLF stages. Learning about CDISC is one thing and is an absolute minimum for any biostatistician. Programming CDISC datasets is a completely different thing and helps make for a GREAT biostatistician. It is one thing to read about something. It is another to practice it. Get out there and program some CDISC datasets (SDTM too, not just ADaM 😊) Happy Tuesday #biostatistics #programming #clinicaltrials ProPharma

Clinical SAS Question ⁉️ 1) Can you explain the concept of Controlled Terminology in CDISC and its significance? #sas #clincialsas #cdisc #sdtm

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📢 Mastering UAT in Clinical Data Management📊 1.5 Edit Check Validation / Data Validation (UAT: User Acceptance Testing)** Data validation is crucial for ensuring that our clinical trial data adheres to protocol specifications, commonly known as UAT (User Acceptance Testing). Here’s a breakdown of this essential process: What: The final phase of database testing, validating the database against protocol specifications. ✅ When: Conducted in a secure, non-study environment before going live. ⏳ Why: To ensure the database performs tasks as required in a real-world setting. UAT is conducted with dummy data to verify its effectiveness. 🔍 Main Steps in UAT: 1. Send edit check programs to the Clinical Programming (CP) team for coding. 2. Define criteria for database performance. 3. Prepare UAT test plan. 4. Design test data covering all functional scenarios. 5. Execute tests, record results, and address any bugs/errors. 6. Retest corrected edit checks. 7. Sign-off once all defects are fixed, confirming the database is ready for real-world use. 📝 Best Practices for Writing Test Cases: 1. Cover all possible functional scenarios. 2. Alternate between query and non-query testing. 3. Use boundary values (+1, -1, =1). 4. Avoid duplicate steps. 💡 Example Test Case Writing: Go to AE FORM, enter AE start date (17/7/24) – Expected: No query – Explanation: No ICF date entered. Go to ICF FORM, enter ICF date (18/7/24) – Expected: Query – Explanation: ICF date should be earlier than AE start date. Go to AE FORM, enter AE start date (+1) (18/7/24) – Expected: No query – Explanation: ICF date and AE date can be the same if the drug is given on the same date as consent. Go to AE FORM, enter AE start date (-1) (16/7/24) – Expected: Query – Explanation: AE start date should be after ICF date. Go to ICF FORM, enter ICF date (17/7/24) – Expected: No query – Explanation: ICF date and AE date can be the same if the drug is given on the same date as consent. Stay tuned for more insights on effective Clinical Data Management! 🌟 #ClinicalDataManagement #UAT #UserAcceptanceTesting #DataValidation #ClinicalResearch #Pharma #QualityControl #BestPractices #ClinicalTrials #DataManagement 💼💡#ClinoSol Research Pvt. Ltd#Amaan Ansari

🎉 Exciting News! 🎉 I am thrilled to announce that I have officially earned my certification as a SAS Certified Professional: Clinical Trial Programming using SAS 9.4! form SAS This certification represents a significant milestone in my professional journey, reflecting my expertise and dedication to the field of clinical trial programming. The exam covered a comprehensive range of topics, including: 1. Clinical Trial Process 2. Clinical Trial Data Structures 3. Regulatory Submissions 4. Manage Clinical Trials Data 5. Transform or Summarize Clinical Trials Data 6. Apply Statistical Procedures for Clinical Trials 7. Macro Programming for Clinical Trials 8. Report Clinical Trials Results 9. Validate Clinical Trial Data Reporting Achieving this certification offers numerous benefits, such as enhancing my credibility in the industry, providing recognition of my advanced SAS programming skills, and equipping me with the knowledge to ensure accuracy and efficiency in clinical trial data analysis. It also opens up new opportunities for career advancement and allows me to contribute more effectively to regulatory submissions and the overall success of clinical trials. I would like to extend my heartfelt gratitude to everyone who supported me throughout this journey. Special thanks to my academic counsellor, Asmita Sorathia, and my professors Ashwini Reddy, @Mahesh Dubey, Akshay Dixit, and @Madhav Desiraju, for imparting their invaluable knowledge and guidance. Your support has been instrumental in achieving this milestone. I am excited to apply the advanced skills and knowledge I’ve gained to contribute to impactful and innovative projects in the healthcare and pharmaceutical industries. #SAS #ClinicalTrialProgramming #Certification #ProfessionalDevelopment #HealthcareInnovation #ClinicalTrials #Pharmaceuticals #CRO #ClinicalResearch #SASProgramming #DataAnalysis #RegulatorySubmissions #Biostatistics #Pfizer #Novartis #Roche #AstraZeneca #GSK #Amgen #Sanofi #CRO #IQVIA #PPD #Parexel #Covance #Medpace

🔍 Mastering Query Management & Data Quality in Clinical Trials🧬 Query Flow & Status: Efficient query management is key to maintaining high-quality data in clinical trials. Here's how we keep things on track: 1️⃣ Open Query: Once a query is raised with a Data Clarification Form (DCF), it’s marked as "Open."   2️⃣ Answered Query: The site reviews source documents and answers the query. The status changes to "Answered."   3️⃣ Re-Query: If further clarification is needed or data is not updated, the query may be re-opened.   4️⃣ Closed Query: Once discrepancies are resolved, the query is marked "Closed," ensuring no further validation failures.   5️⃣ Irresolvable: If a discrepancy can’t be resolved, it’s updated as "Irresolvable" in the discrepancy database.   6️⃣ Auto-Closed: Automatic closure of queries when all criteria are met. Quality Assurance (QA) & Quality Control (QC): 🔍 Quality Control (QC): Real-time review to ensure data accuracy and validity. This involves ongoing checks to ensure data follows guidelines, is correctly entered, and matches source documents. 🛡️ Quality Assurance (QA): Planned, systematic activities to prevent errors during database design, UAT, and other processes. QA ensures compliance with regulations and provides valuable insights to management by identifying problems and trends. By focusing on both QA and QC, we ensure that the data generated during research is not only accurate but also meets all regulatory requirements. #ClinicalTrials #DataManagement #QualityAssurance #QualityControl #ClinicalResearch #Pharma#Amaan Ansari#Mujeebuddin Shaik#ClinoSol Research Pvt. Ltd

🎉 Thrilled to share that I’ve earned the SAS® Programming for Clinical Trials badge! 🏅This achievement has enhanced my understanding of clinical data analysis, regulatory standards, and SAS programming techniques, all of which are critical in clinical trial processes.I'm excited to apply these skills in future projects and contribute to advancing healthcare data analysis.#SAS #ClinicalTrials #DataAnalysis #ClinicalData #Pharmaceuticals #Healthcare #ContinuousLearning #SASProgramming #CareerGrowth. View my verified achievement from SAS.

🌟 **Unlock the Power of Clinical SAS in the Pharma Industry!** 🌟 Follow 👉Pankaj Salapurkar for more information 🎓⏰✅ Are you ready to dive into the world where data meets healthcare? Clinical SAS is the driving force behind the analysis and reporting of clinical trial data. As a SAS professional, you hold the key to advancing drug development and patient safety. 🔑 Why Clinical SAS? 💠 Data-driven Insights:- Analyze large datasets to draw conclusions that help in making informed decisions. 💠 Compliance:- Meet regulatory standards like FDA or EMA with standardized data structures (SDTM, ADaM). 💠 Career Growth:- The demand for skilled SAS programmers is on the rise in pharmaceutical companies, CROs, and regulatory bodies. 🚀 Key Areas You’ll Learn:-🚀 1.Data Management:- Learn how to manage, clean, and transform clinical trial data. 2.Data Reporting:- Master creating reports, tables, listings, and graphs for regulatory submissions. 3.CDISC Standards:- Get familiar with SDTM, ADaM, and TLFs to streamline your work. 🌍 The Future is Data, and YOU can be a part of it! 💡 Ready to elevate your skills in Clinical SAS? Join my course and take your first step towards becoming a Clinical SAS expert. Follow 👉Pankaj Salapurkar for more information 🎓⏰✅ #HappyLearning #ClinicalSAS #DataScience #ClinicalTrials #PharmaCareers #CDISC #SASProgramming #DataAnalytics #StatisticalProgrammers #connections #Knowledge #Sharing

Question: Is it possible to 100% remove these error types from #EQA submissions in clinical #pathology? - Transcription error 🖋 - Transposition error ✏ - Results submitted after the due date 🕟 - Reported incorrect units 📏 - Wrong method code 👨💻 - Incomplete data submitted 💻 - Reported results under wrong analyte 🔬 Answer: "Yes!"... get in touch! Regardless of LIMS/LIS system, regardless of EQA/ #proficiencytesting provider terence.smith@x-labsystems.co.uk #pathology #diagnostics #Lab2lab #interoperability 

Parameter Type  (PARAMTYP) PARAMTYP is a variable within the CDISC ADaM (Analysis Data Model) standards used in clinical research. Its purpose is to flag whether a specific parameter is derived from other parameters or not. Let's break down its definition: Definition: PARAMTYP stands for "Parameter Type." It indicates whether a parameter is derived or not. If a parameter is derived from other ADaM parameters, then PARAMTYP is set to "DERIVED." Otherwise, it remains null. Consistency: PARAMTYP maintains the same constant value (either null or "DERIVED") across all rows of a given parameter. This ensures uniformity in indicating whether the parameter is derived or not throughout the dataset. Row Classification: whether PARAMTYP is "DERIVED" or null, the rows within the parameter can still be classified as "observed" or "derived" . #sas #cdisc #adam #adsl #sdtm #cdisc #clinicaltrial #sasprogrammimg