Sunil Gupta’s Post

7 Essential Skills for a Successful Clinical SAS Programmer 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐒𝐀𝐒 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 is crucial for 𝐚𝐧𝐚𝐥𝐲𝐳𝐢𝐧𝐠 & 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐢𝐧𝐠 complex healthcare data in clinical trials. 📈 𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐨𝐭𝐡𝐞𝐫 𝐬𝐤𝐢𝐥𝐥𝐬 𝐚𝐫𝐞 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐟𝐨𝐫 𝐬𝐮𝐜𝐜𝐞𝐬𝐬? 🤔 🏆 Proficiency in SAS Programming 📊 Data Management Skills 📚 Knowledge of CDISC/SDTM 📈 Familiarity with Statistical Analysis 🗣️ Strong Communication Skills 👀 Attention to Detail Ready to launch your career in  𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐒𝐀𝐒 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠? 𝐄𝐧𝐫𝐨𝐥𝐥 𝐢𝐧 𝐂𝐥𝐢𝐧𝐢 𝐋𝐚𝐮𝐧𝐜𝐡'𝐬  comprehensive courses and gain the expertise you need! ➡️ #clinicalsasprogrammer #sasprogramming #clinicalresearch #clinicaltrials #healthcare #dataanalysis #statistics #cdisc #sdtam #communication #attentiontodetail #clinilaunch #clinicalsascourse #careeradvancement #healthcarecareers #pharmaceutical #biotechnology

Clinical SAS Programmer - Irving, TX Katalyst HealthCares & Life Sciences Overview: Clinical trial objectives, design, endpoints, and procedures defined in Protocol and Statistical Analysis Plan (#SAP). Develop programs that convert raw #SAS datasets into standard format and improve existing programs by creating standard programs and macros. #CDISC guidelines required for regulatory submissions. Independently program #CDSIC #ADaM / #ADS datasets and Tables Listings and figures (#TLFs) as per requirement of study including efficacy related outputs. Provide validation support to create #ADS or #SDTM+, #SDTM and #ADaM datasets and #TLFs & documentation as per the specifications within set timelines Good Clinical Practice (#GCP) and International Conference on Harmonization (#ICH) guidelines .Create #ADS / #ADAM datasets from #RAW / #SDTM datasets according to project / #CDISC analysis data model standards; Independently write, test, run. Reach me @908-373-5281 ll [email protected] #CDSIC #ADaM #ADS #SDTM #SAP #w2 #USA #ADS #CDISC #Genetherpeutics #Rprogramming #SASMacros, #SAS #STAT, #SAS #Graph, #SAS #SQL #SAS #ODS.#USA #W2 #Statistics, #Mathematics #Biotechnology #Pharmaceutical You can Apply Here:https://shorturl.at/eijEQ

Metadata-driven SAS Code Generation How useful would it be to programmatically generate well-tested #sas code that performs data transformations across known data formats? Here's the context: Life sciences companies use SAS code extensively to perform many data transformation from format A to format B. At its most basic, a SAS macro is "take this file, uppercase field 12, and split field 13 into these 02 other fields" and such. Our Data Standards Governor platform can store the metadata for dozens of data formats. Thus, we can auto-generate the SAS macros to transform data from format A to format B. For example: If we know the data format definition of Lab Vendor 01, then we’ll generate the SAS macro that transforms the vendor’s data into SDTM. Same with up-versioning source data. If data is in SDTM 2.x we'll programmatically create the SAS macros that transform the data into SDTM 3.x These SAS macros are useful because many pharma companies have validated SAS environments, sponsors just need to reduce the need for FTEs who write the SAS macros by hand. Our goal is to deliver the most value possible while creating the smallest amount of change for clients' existing operations. #clinicaldatamanagement #clinicaltrial #clinicaltrialmanagement #datagovernance #guidelines #lifesciences #pharmaceutical #regulatoryaffairs #regulatorycompliance #sas #standards #standardsdevelopment #terminology

More and more statistical programmers and statisticians are signing up for the SAS to R course. It starts tomorrow with the first live training by the experienced R programmer and trainer Thomas Neitmann - of course we will record it for you! Not just those who work only with clinical data, but also those who work with observational studies and other RWE data. The bulk of the course helps here as well. While we will cover CDISC in particular, the rest of the course will be very relevant for anyone working on RWE. First, of course, we cover the move to an open source language. The typical data visualizations used in medicine will be another focus. We will also discuss the different, most commonly used analysis methods and the typical tables for them. These are mostly identical for clinical studies and for RWE. One of the RWE programmers who signed up for the course said: "This course is closer to my need than many other courses out there." Do you want to join this Pharma and CRO course with your colleagues to learn R?? You can find the link to register for The Effective Statistician SAS to R course in the comments. Photo by Anna Voelske.

📚Clinical SAS Interview Question: Steps to Generate SDTM Datasets📚 Following are the steps to generate SDTM datasets from raw data. 📌Define Variables and Domains: Begin by reviewing the SDTM documentation to identify the necessary variables and domains based on the data collected during the clinical trial. Common domains typically include Demographics (DM), Adverse Events (AE), Exposure (EX), Disposition (DS). Transform Raw Data: Use of SAS programming techniques to convert the raw data into SDTM datasets. This process will involve tasks such as data cleaning, variable mapping, and implementing SDTM-specific rules and formats. 📌Apply SDTM Guidelines: Ensure adherence to CDISC (Clinical Data Interchange Standards Consortium) guidelines and specifications for each domain. Properly assign variables to their respective datasets and domains following the CDISC standards. 📌Perform SDTM Validation: Validate the generated SDTM datasets by running comprehensive checks. This includes comparing the transformed data against SDTM guidelines and executing consistency checks to ensure the accuracy and completeness of the datasets. 📌Generate Define.XML and Documentation: Produce Define.XML files and accompanying documentation. These files provide essential metadata and detailed descriptions of the SDTM datasets, which are crucial for regulatory submissions and data interpretation. Contact us via WhatsApp at +91-7488076778 if you need assistance from us to get you industry-ready. #clinicalsas #sas #sdtm #cdisc #biostatistics #statisticalprogramming

Open Letter to the Pharma Industry during this Month of Giving Thanks   I can’t believe we have been in business 6 years this month. I feel very fortunate to have been able to run a successful pharma analytics business. When I started, I could not have imagined the impact of something like Covid, but we are still here going strong. In this month of giving thanks, I want to thank all the people that allowed me to grow to this point in my career. I'd like to thank all of my bosses and mentors as well as the colleagues I have worked with. It has been an incredible journey. I also want to thank my clients who have the resources to hire anyone, but choose to work with us. I cannot thank you enough.   While I am really fortunate and blessed that Pharmafusion has done so well, I have to say I have failed epically at communicating our value proposition and how we are fundamentally different from every other company or internal team trying to do what we do.   I would welcome the opportunity to share my angst with a professional adept at messaging. But let me try to share a few things and explain why our platform and approach is so revolutionary, and why I stink at communicating it.   We have struggled to clearly explain what we are and why we are better.  So let me do the opposite and explain what we are not. And let me keep it real.   We are not a typical “Data Company” with multiple account managers adept at managing accounts, but lacking the skills to provide insights when you need them.   All of our employees must go through the 3-level boot camp of Pharma Commercialization, Analytics & Data Visualization, and Data Management with Coding. When you talk to someone at Pharmafusion they can help you NOW, because they have these 3 skill sets.   We are NOT a company cobbled together from the remnants of multiple consulting practices purchased by a venture fund. We are ONE company created with one singular vision and one turnkey solution engineered to deliver one significantly better analytical experience. Our vision is a single platform that has all of the Pharma Analytics in one spot to run the enterprise.     Whether you are C-Level, Bean Counter, Market Access, Trade, Salesperson, IT, or a Scientist, you can all gather ‘round the campfire that is the Pharmafusion360 platform.   If you are ready for a completely new and invigorating experience with your data, without all the marketing and typical life science data BS, give us a call.    Give us a week and you will get an entirely new experience. We can make this one promise if you do call. We will “keep it real and it will be awesome." At least that is what our clients say.

How to Save Time and Money with Clinical Data Analytics: A Simple and Effective Approach When it comes to clinical studies and clinical data management, the famous Rolling Stones song title "Time Is On My Side" doesn't apply. Tons of clinical studies run for less than a year. Some safety studies last only a few weeks. Time is definitely *not* on our side. Setting up clinical data analytics systems to optimize quality, cost, and speed is crucial. I want to share a simple and cost-effective approach that supports critical use cases that clinical operations, data management, and medical monitoring teams need. The pattern can be implemented in a number of ways, but I've found the combination of PostgreSQL, scripted automation, and a great analytics environment does the trick. It starts with storing a snapshot of your EDC in a database every day and writing some basic Structured Query Language (SQL) statements that target clinical operations, data management, and medical monitoring use cases. One set of SQL SELECT statements targets data management and clinical operations use cases. The outputs include the time variable, usually the date of the EDC snapshot, and help teams understand how the EDC data is changing over time. This may include %SDV analytics, query analytics, and listings of missing data. The ability to monitor inconsistencies and trends in data over time helps a team clean data faster, monitor query trends, and communicate with sites and partners more effectively. The other set of SQL doesn't require a time variable as much as it requires the most recent set of clean EDC data. These SQL outputs target medical monitoring teams and provide the data for safety signal monitoring, efficacy outcome analyses, and report generation for medical writing and regulatory submissions. Don’t let time be the enemy. Make it your ally!  If you are interested in learning more about how this simple and effective approach can help with your clinical data analytics, don’t hesitate to reach out to me over LinkedIn or email at [email protected]. #clinicaldatamanagement #edc #sql #analytics #visualization

Our colleague Kai Wanke will be presenting at the ACDM24 in Copenhagen. Here's his abstract: Mapping Engine- An Approach to Standardising SAS Programming in Clinical Trials One of the difficulties when working with clinical trial data is the variability of source data generated in different trials or by different vendors poses one of the major challenges faced when working with clinical trial data. Often this is the case even when the same EDC system is used throughout. To ensure interoperability data is expected to be converted into standardised models before it is analysed and submitted to regulatory bodies. This process presents challenges since it can be time-consuming and may introduce a degree of variability, for example between data generated between different study teams. To address these challenges, we have developed a data conversion framework. This framework is a suite of SAS macros that uses pseudo-code stored in a spreadsheet (called ‘mapping specifications’) to generate SAS programs that perform the conversion of raw data into specific models, such as SDTM or similar data models which may be company-specific. This framework is referred to as Mapping Engine. As part of its specifications, Mapping Engine consumes metadata to ensure all its created datasets and variables adhere to the specifications of the selected model such as data types, labels, formats, et cetera. One of the benefits of working with Mapping Engine is that its specifications are organised around individual source datasets and variables rather than complete trials, making it highly transferable and reusable between files, users, and trials. Moreover, this transferability allows the creation of pre-defined libraries of mapping specifications that are tailored around a specific vendor, therapeutic area, or compound, and describe how the data should be processed. Using SAS code, it is possible to automatically match the raw data against the mapping libraries, automatically generating an initial version of the specifications that can be used to convert the raw data into the desired model. This results in the automated, metadata-driven conversion of clinical trial data for up to 90% of items in case report forms. Mapping Engine allows to minimise the manual effort needed during data conversion, while at the same time enforcing programmatic standards and reducing variability between different studies. Are you interested in a demo of want to know more about? Get in touch with Jules van der Zalm or Melanie Schopp. #SDTM #conversion #clinicaltrials #statisticalprogramming #automation

CDISC Standards in AKI Research Highlight the Problem and Solution Start by emphasizing the challenges of inconsistent and poorly organized data in AKI research. Briefly introduce CDISC standards as a solution for ensuring data quality and streamlined analysis. List the key benefits of using CDISC standards: improved data quality, efficient data organization, and enhanced collaboration Real-world Example (Optional) Consider including a concise example of how CDISC standards helped a specific AKI research project. This will make the concept more relatable to your audience. Call to Action Encourage discussion by posing a question related to CDISC standards or AKI research. "Have you implemented CDISC standards in your research? Share your experiences in the comments below!" #CDISC #AKIResearch #ClinicalTrials #DataQuality Tired of inconsistent data hindering your AKI research? CDISC standards can be your game-changer! They provide a common language for data collection and organization, ensuring quality and streamlining analysis. Benefits of CDISC standards: Improved data accuracy and consistency Efficient data organization and sharing Enhanced collaboration between researchers Have you implemented CDISC standards in your research? Share your experiences in the comments below! #CDISC #AKIResearch #ClinicalTrials #DataQuality https://lnkd.in/gba6235K

I have some advice for people who are set in their ways with SAS but want to transition to R for some projects. Don't get me wrong, I love SAS. But sometimes it isn't available. Then what? For example, I am currently analyzing some clinical trial SAS Xport files in CDISC format but the workspace I am using does not have SAS. Don't panic if this happens to you. Just use R. The haven library lets you read other file formats into R: https://lnkd.in/eDHx4-aX Works like a charm. This book is top notch. It tells you all about making tables in R, specifically for the clinical trials use case, and includes a sample data set. https://lnkd.in/edbq8MeM This is a brilliant and super-handy comparison of SAS vs R. Excellent write up: https://lnkd.in/egaGZuHP If you have a whole folder full of these SAS files and feel overwhelmed, you can make a quick R Shiny app for exploratory data analysis. This blog describes it very well: https://lnkd.in/eJSQhwKk My heart belongs to Python, but R is not far behind. I recommend learning several languages because it makes it so much easier to work with different teams. I also recommend learning SPSS and STATA (often overlooked by computer science folks) because many clinician researchers learn those.