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sdtm programming

102 papers and 17 presentations; 118 titles.

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Conference × Year × Title × Authors Extras Pages
WUSS 2026 A Flexible Metadata-Driven and Modular Approach to SDTM Automation [presentation] Ruili Shi,
Jiaying Zhang,
Charles Chen		 source 15
PharmaSUG CN 2026 AI-Augmented SDTM Workflow: A Methodological Framework Yuqi Chen,
Muqu Lv,
Zhuozhao Zhan		 source 16
PharmaSUG CN 2026 AI-Native Real-Time SDTM Mapping and Exploration Agent Hao Chen,
Xuejie Zhou,
Haomin Yang,
Qianwang Wang		 source 13
PharmaSUG CN 2026 Automating SDTM Programming with a Human-in-the-Loop Multi-Agent System Wenqiang Chu source 7
PharmaSUG CN 2026 Modernizing the Vaccine TAUG: An End-to-End, Output-Driven Framework for Reactogenicity, Immunogenicity, Efficacy, and Reviewer-Ready Submissions Lin Chen,
Jialiang Liu		 source 18
PharmaSUG 2026 Closing the Loop: Validating AI-Generated SDTM Mappings using CDISC CORE and Synthetic Data Pietro Belligoli,
Constantin Weberpals,
Yarhy Flores Lopez		 source 7
PharmaSUG 2026 Practical Lessons in AI-Assisted Metadata Conversion: From Database Specs to EDC and SDTM - Successes, Pitfalls, and Practical Implementation Jianlin Li,
Andy Shen		 source 11
PharmaSUG 2026 Virtual Data, Real Standards: Leveraging Data Simulation for Smarter Clinical Trials Sangeeta Shabadi,
Nitesh Patil,
Jonathan Henshaw		 source 16
PHUSE US 2026 Enhancing Clinical Trial Dataset Mapping with AI/ML Techniques: An Approach for CDISC Variable Prediction Parvee Kumar source
presentation source		 7
PHUSE US 2026 From Black Box to Glass Box: A Context-Aware Approach to SDTM Mapping Automation with Interactive Visual Validation Rostislav Markov,
Jacques Lanoue		 source
presentation source		 12
PHUSE US 2026 Metadata-Driven TFL Generation: End-to-End Automation from Protocol to Output Bill Qubeck source
presentation source		 12
PHUSE US 2026 Not Another Other: Bridging Data Collection with SDTM Mapping Without Open Text Fields Aminta Jimenez source
presentation source		 6
PHUSE US 2026 Reimagining SDTM Mapping with Agentic AI and Automation Nathan Johnson source
presentation source		 4
PHUSE US 2026 Rule-Based Auto-SDTM Mapping Software in Python, Which Automatically Generates Executable SAS or R Programs Carrie Zhang,
Bill Zhang		 source
presentation source		 9
PHUSE APAC 2026 Automated Comparison of EDC Transfers: Identifying Data Changes Across Versions Malavika Biju,
Vignesh V. Gopal,
Rahulraj Raju		 source
presentation source		 6
PHUSE APAC 2026 Elevating transparency in data standardization Applying XAI in SDTM, ADaM, and TLF Generation Shakila Jagadeesan source
presentation source		 10
PHUSE APAC 2026 Optimizing Vendor Data Mapping: A DTS-Driven Approach to SDTM Readiness Chandan Patel Malyala,
Prasad Attini		 source
poster source		 4
PHUSE APAC 2026 RIP – Inconsistent Metadata Specifications with RAP (R And Python) Generate Specs Faster and Detect Early Errors to Speed Up Submissions Kairav Tarmaster source
presentation source		 8
SESUG 2025 Ensure complete raw data to SDTM mapping: a SAS macro approach for quality control Cally Davidson,
Eric Howard		 8
PharmaSUG 2025 Use Gen AI to program rules in R & Python to generate and validate metadata for data standards and study specifications. Priyanka Sawant,
Kairav Tarmaster		 source 9
PharmaSUG 2025 {sdtm.oak} V0.1 on CRAN, sponsored by CDISC COSA, part of Pharmaverse, is an EDC and Data Standard agnostic solution for developing SDTM datasets in R. Rammprasad Ganapathy source 5
PHUSE US 2025 A Guided User Experience for Automated Mapping Nathan Johnson source
presentation source		 9
PHUSE US 2025 Innovative Storage of SDTM Mappings as JSON: Boosting Efficiency and Data Consistency Rohan Parmar,
Edgar Manukyan		 source
presentation source		 5
PHUSE US 2025 Let's Talk Future: Automated Solution for Standard-Compliant Data, Mock Shells and TFL Generation Using Open-Source Platforms with AI Inderjeet Arora,
Abhijeet Arora		 source
presentation source		 8
PHUSE US 2025 SDTM Specification Automation Using an ML Approach to Provide Mappings Benzi Mathew,
Jaya Simha Inampudi		 source
presentation source		 5
PHUSE US 2025 {sdtm.oak} - SDTM Programming in R Rammprasad Ganapathy,
Shiyu Chen		 source
poster source		 5
PHUSE EU 2024 A Guided User Experience for Automated Mapping Achilleas Zaras source
presentation source		 9
PHUSE EU 2024 Behind the Blind: Enhancing Clinical Data Management with Python Niels Schrauwen source
presentation source		 5
PHUSE EU 2024 How R Pharma? An Overview of How Our Industry Is Adopting the R Programming Language Ross Farrugia,
Ari Siggaard Knoph		 source
presentation source		 5
PHUSE EU 2024 Mastering Pinnacle 21 for Oncology SDTM Daryna Khololovych source
presentation source		 7
PHUSE EU 2024 OAK Productivity Experiment - How We Made SDTM Programming 5x Faster Amy Franklin,
Magdalena Krochmal		 source
presentation source		 5
PHUSE EU 2024 The Significance of Generic Data Transfer Specification for Non-CRF Data in Clinical Trials Samaneh Sabiani,
Kristina Zweier		 source
presentation source		 9
PharmaSUG CN 2024 An Automatic SDTM Platform: Create Your SDTM Since Case Report Form is Ready! Mingyue Zhu,
Wei Wang,
Mingfeng Zhou		 source 12
PharmaSUG CN 2024 CDASH-SDTM Combo Model in SDTM Automation Chunpeng CZ Zhao source 11
PharmaSUG 2024 A New Approach to Automating the Creation of the Subject Visits (SV) Domain Xiangchen (Bob) Cui,
Jessie Wang,
Min Chen		 source 24
PharmaSUG 2024 A Practical Approach to Automating SDTM Using a Metadata-Driven Method That Leverages CRF Specifications and SDTM Standards Xiangchen (Bob) Cui,
Min Chen,
Jessie Wang		 source 23
PharmaSUG 2024 Challenges and Considerations When Building e-Submission SDTM Data Packages Wei Duan source 8
PharmaSUG 2024 Cultivating Success with Non-standard Investigator-sponsored Trial Data for FDA Submissions Jai Deep Mittapalli,
Jinit Mistry,
Venkatesulu Salla		 source 9
PharmaSUG 2024 Handling of Humoral and Cellular Immunogenicity Data in SDTM Wei Duan source 6
PharmaSUG 2024 Protocol Amendments and EDC Updates: Downstream impact on Clinical Trial Data Anbu Damodaran,
Ram Gudavalli,
Kumar Bhimavarapu		 source 11
PharmaSUG 2024 Streamlined EDC data to SDTM Mapping with Medidata RAVE ALS Yunsheng Wang,
Erik Hansen,
Chao Wang,
Tina Wu		 source 12
PHUSE US 2024 A Practical Approach to Automating SDTM Using a Metadata-Driven Method that Leverages eCRF Specifications and SDTM Programming Standards Xiangchen (Bob) Cui,
Min Chen		 source
presentation source		 12
PHUSE US 2024 Data Handling through CDISC Standards in Master Protocols: A Focus on Rescreened and Re-Enrolled Subjects Swathi Kotla,
Madhusudhan Reddy Papasani		 source
presentation source		 7
PHUSE US 2024 Effective Workflow Process: Rapid SDTMs and Compliance Dashboards Sunil Kumar Gupta,
Harinarayan Gopichandra		 source
poster source		 5
PHUSE US 2024 Enabling the Interoperable Data Ecosystem with the elluminate Clinical Data Cloud [presentation] Nathan Johnson source 15
PHUSE US 2024 Standards Library: Getting started - A Medical Device Case Study Amy Booth source
presentation source		 6
PHUSE EU 2023 Implementation of Estimands Using Data Standards [presentation] Chris Price,
Alexandra Cochinaire		 source 26
PHUSE EU 2023 MINT+ Automation and Digitization of SDTM specification Magdalena Krochmal source
presentation source		 5
PHUSE EU 2023 Oak Garden - Metadata-Driven SDTM Automation in R Magdalena Krochmal,
Amy Franklin		 source
presentation source		 7
PharmaSUG 2023 Automation of the SDSP CBER Appendix for Vaccine Studies Nicole Jones,
Pritesh Solanki		 source 6
PharmaSUG 2023 Better CDISC Standards with Metadata Programming Sunil Kumar Gupta,
Abhishek Dabral		 source 14
PharmaSUG 2023 Can We Do It Better? Real-Time Validation Of Sdtm Mapping Is Superior To Double Programming Daniel Rolo,
Bremer Louw		 source 9
PharmaSUG 2023 SDTM Variables You Might Forget About Nadiia Pukhliar,
Dariia Tsyhanenko,
Iryna Kotenko		 source 10
PHUSE US 2023 Balance Between Humans and Machines - How NLP Helps Translate Text Specification to Code, and Vice Versa Hao (Harry) Chen,
Jaimin Patel		 source
presentation source		 9
PHUSE US 2023 Implementation of Estimands (ICH E9 (R1)) Using Data Standards [presentation] Banoo Madhanagopal,
Lori Van Meter		 source
video		 32
PHUSE US 2023 SDTM Mapping Recommendation Engine - Acorn [presentation] Rohan Parmar,
Harshit Manektalia		 source
video		 32
PHUSE EU 2022 Clinical Data Standards and Production of SDTMs. Are We Missing Important Points? [presentation] Eva Stigson,
Paulina Kuczkowska		 source 15
PHUSE EU 2022 If you want to deliver standardised clinical data sets quickly, talk to the machine Chris Austin,
Michael Phillips		 source
presentation source		 6
PHUSE EU 2022 Minimising 'Data Loss' During Mapping of Raw Data to SDTM [presentation] John Monaghan source
video		 35
PharmaSUG 2022 Applying Agile Methodology to Statistical Programming Gina Boccuzzi source 5
PharmaSUG 2022 Checking Compliance of CDISC SDTM Datasets Utilizing SAS Procedures. Ballari Sen source 17
PharmaSUG 2022 Redefining Industry Standards: Making CDASH and SDTM Work Together from the Database Level Chad Fewell,
Jesse Beck		 source 5
PHUSE US 2022 Incorporating Immunogenicity Data from Various Analyzing Labs [presentation] Elisabeth van Son-Palmen,
Gunasekaran Subramaniam		 source 19
PHUSE US 2022 Metadata-driven Case Report Tabulation for Regulatory Submissions Jacques Lanoue,
Rajashekar Thota		 source
presentation source		 8
PHUSE EU 2021 Code Sharing is Caring [presentation] Lucy Palmen source
video		 16
PHUSE US 2021 Controlled Terminology Best Practices [presentation] Sarah Angelo source
video		 29
PHUSE US 2021 SDTM Mapping and Conversion Using NLP [presentation] Eric Weising source
video		 16
PHUSE US 2021 Standing Room Only Presentation: DS06: CDISC Analysis Results Standard [presentation] Bess LeRoy source 25
PHUSE US 2021 VALSPEC Utility to Validate CDISC Specifications [presentation] Aman Bahl,
Hrideep Antony		 source
video		 12
PHUSE EU 2020 Digital Data Flow - A Solution for Study Start-up and Amendments [presentation] Palanipandiyan Chokkalingam,
Srinivasan Mahalingam		 source
video		 22
PHUSE EU 2020 Don't UnderMine the Value of Your CRF - with Perl Regular Expressions [presentation] John McDade source
video		 22
PHUSE EU 2020 SDTM Mapping and Conversion Using ML/NLP [presentation] Eric Weising,
Farha Feroze		 source
video		 15
PHUSE EU 2020 SDTM: Let's Read Outside the Bible [presentation] Sandra Latorre source
video		 23
PharmaSUG 2020 Automating CRF Annotations using Python Hema Muthukumar,
Kobie O'Brian,
Julie Stofel		 source 9
PharmaSUG 2020 Untangling the Subject Elements Domain Christine McNichol source 8
PHUSE US 2020 Case Study: Integrated SDTM to Integrated ADaM to TLFs Kishore Pothuri source
presentation source		 6
PHUSE US 2020 Feature of SDTM Mapping and Conversion Using NLP Eric Weising,
Shiva Katepalli		 source
presentation source		 3
PHUSE US 2020 Mind the Gap: Raw Data Model Julie Smiley source
presentation source		 7
PHUSE US 2020 The Elusive Goal of Automation in SDTM Mapping: A CRO perspective Sridhar Vijendra,
Ephicacy Lifescience		 source
presentation source		 6
PHUSE US 2020 Untangling the Subject Elements Domain Christine McNichol source
presentation source		 7
PHUSE EU 2019 End-to-end SDTM Automation: A Metadata-centric Approach (winner) Roman Radelicki source
presentation source		 12
PHUSE EU 2019 Making data mapping process easier and smarter with SAS and R Andrii Artemchuk source
presentation source		 10
PHUSE EU 2019 Mapping Raw Data to SDTM Define Using the SAS Clinical Standards Toolkit Andrew Bullivant source
presentation source		 7
PharmaSUG 2019 Automate the Process to Ensure the Compliance with FDA Business Rules in SDTM Programming for FDA Submission Xiangchen (Bob) Cui,
Hao Guan,
Min Chen,
Letan (Cleo) Lin		 source 16
PharmaSUG 2019 Homogenizing Unique and Complex data into Standard SDTM Domains with TAUGs Sowmya Srinivasa Mukundan,
Charumathy Sreeraman		 source 10
PHUSE US 2019 Don't Let Your Data Handle You: A Novel Approach to Clinical Programming Jorine Putter source
presentation source		 10
PHUSE US 2019 End-to-End SDTM Automation: A Metadata-centric Approach Roman Radelicki,
Swapna Pothula		 source
presentation source		 12
PHUSE US 2019 Streamline Process: To Generate SDTM Programs by Automation Hemalatha Elumalai source
poster source		 8
SESUG 2018 A User Defined SDTM Data Quality Checking and Tracking System Zemin Zeng,
Yunzhi Ling		 source 6
PHUSE US 2018 Increase Quality and Reduce SDTM Development Time with a Test Study Data Simulator Sasikumar Palanisamy,
Bhaskar Subramanian		 source 4
PHUSE US 2018 Want Submission-Ready Datasets Package from the Get-Go? Kishore Pothuri,
Bhavin Busa		 source 7
PharmaSUG 2018 Challenges in Implementing Device Related ADaM and SDTM Standards in a Drug-Device Study Wenying Tian,
Maureen Maunsell,
Karin LaPann		 source 9
PharmaSUG 2018 Preventing premature unblinding in PK/PD related studies Debbie Sisco,
Alice Zong		 source 11
PharmaSUG 2018 Want Submission-Ready Datasets Package from the Get-Go? Kishore Pothuri,
Bhavin Busa		 source 8
PharmaSUG 2017 Building a Fast Track for CDISC: Practical Ways to Support Consistent, Fast and Efficient SDTM Delivery Steven R. Kirby,
Mario Widel,
Richard Addy		 source 14
PharmaSUG 2017 Data Integrity: One step before SDTM Pavan Kathula,
Sonal Torawane		 source 9
PharmaSUG 2017 Leveraging Study Data Reviewer-s Guide (SDRG) in Building FDA-s Confidence in Sponsor-s Submitted Datasets Xiangchen (Bob) Cui,
Min Chen,
Letan (Cleo) Lin		 source 10
PharmaSUG 2017 Planning to Pool SDTM by Creating and Maintaining a Sponsor-Specific Controlled Terminology Database Cori Kramer,
Ragini Hari,
Keith Shusterman		 source 7
PHUSE EU 2016 How to Create your Define-XML as Early as Possible with the Click of a Button Roman Radelicki source
presentation source		 7
PharmaSUG CN 2016 Automation of SDTM Programming in Oncology Disease Response Domain Yiwen Wang,
Yu Cheng,
Ju Chen		 10
PharmaSUG 2016 "It is a standard, so it is simple, right?": Misconceptions and Organizational Challenges of Implementing CDISC at a CRO Susan H.M. Boquist,
Adam J. Sicard		 source 5
PharmaSUG 2016 A Practical Approach to Re-sizing Character Variable Lengths for FDA Submission Datasets (both SDTM and ADaM) Xiangchen (Bob) Cui,
Min Chen		 source 19
PharmaSUG 2016 A SAS Macro Tool to Automate Generation of Define.xml V2.0 from SDTM Specification for FDA Submission Min Chen,
Xiangchen (Bob) Cui		 source 24
PharmaSUG 2016 Automatic Consistency Checking of Controlled Terminology among SDTM Datasets, Define.xml, and NCI/CDISC Controlled Terminology for FDA Submission Min Chen,
Xiangchen (Bob) Cui		 source 13
PharmaSUG 2016 Duplicate records - it may be a good time to contact your data management team Sergiy Sirichenko,
Max Kanevsky		 source 8
PharmaSUG 2016 SDTM Automation with Standard CRF Pages Taylor Markway source 10
PHUSE EU 2015 E2E Data Standards: The Need for a New Generation of Metadata Repositories Isabelle de Zegher source
presentation source		 4
PharmaSUG 2015 A Toolkit to Create a Dynamic Excel Format Metadata to Assist SDTM Mapping Process Huei-Ling Chen,
Helen Wang		 source 11
PHUSE EU 2014 From ODM to SDTM: An End-to-End Approach Applied to Phase I Clinical Trials Alexandre Mathis source
presentation source		 5
PHUSE EU 2014 SDTM Mapping Becomes more EASY with SAS Clinical Data Integration Saumilkumar Tripathi source
presentation source		 8
PHUSE EU 2013 Are You out of Your Mind? CDISC Mappings Using the Mind Map Tool Niels Both,
Johannes Ulander		 source
presentation source		 5
PharmaSUG CN 2012 Using HASH Table to reduce the use of merge for large datasets Long Fang 7
PharmaSUG 2012 Automatic Detection and Identification of Variables with All Missing Values in SDTM/ADaM Datasets for FDA Submission Min Chen,
Xiangchen (Bob) Cui		 source 7
PharmaSUG 2011 A Cost-Effective SDTM Conversion for NDA Electronic Submission Xiangchen (Bob) Cui,
Scott E. Moseley,
Min Chen		 source 12
PharmaSUG 2011 Truncation, Variable Association, Controlled Terminology, and Some Other Pitfalls in the SDTM Mapping Process Na Li,
Gary de Jesus,
Daniel Bonzo		 source 8
PharmaSUG 2010 Quality in a World of "Standards" with Differences! Sandra VanPelt Nguyen 9
NESUG 2008 An Approach to CDISC SDTM Implementation for Clinical Trials Data William Chen,
Margaret M. Coughlin		 5
PHUSE EU 2007 All roads lead to SDTM - which one shall we take? Ian Fisher,
Donna Thompson		 source 9
SGF 2007 Extending SAS Data Integration Studio with Java: Custom GUIs and SAS Server Interactions Christopher Treglio source 15

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