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mock shell

60 papers and 7 presentations; 66 titles.

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Conference × Year × Title × Authors Extras Pages
PharmaSUG CN 2026 ShellPainter & CDARS: An AI-Powered Digital Framework for Integrated shell-TFL Automation Jiapeng He,
Mingliang Fan,
Yanan Sui,
Lin Jiang,
Mingfeng Zhou,
Xuejie Zhou		 source 7
PharmaSUG 2026 Friction to Flow: LLM-Based Automation of Clinical Data Workflows Pankaj Attri,
Matthew J. Becker		 source 8
PharmaSUG 2026 It-s a Wonderful Lifecycle: Translating Statistical Programming into Modern Analytics Development Steve Nicholas source 3
PharmaSUG 2026 Leveraging LLMs in Data Science Web Applications: Beyond the Chat Interface Tyler Rowsell,
Nandini Thampi,
Dishank Jani,
Hao Xu		 source 18
PharmaSUG 2026 Oncology Solid Tumor Subcutaneous vs Intravenous Late-stage Study Analysis Sumanjali Mangalarapu,
Chuqing Chen,
Anilkumar Anksapur		 source 11
PharmaSUG 2026 The Tipsy Hangover: Avoiding Indent Headaches in SAS Reports Lisa A. Mendez,
Richann J. Watson		 source 22
PHUSE US 2026 Automating Analysis Report Programming in SAS with VGSART: A Metadata-Driven Approach Swathi Nattam,
Piyush Makkapati		 source
presentation source		 12
PHUSE US 2026 Consistency, Speed and Satisfaction: The Impact of TLF Mock Shell Centralisation Jie Yang source
presentation source		 6
PHUSE US 2026 LLM-Powered Clinical Data Flow with Traceability - From Siloed Pipelines to a Semantic Fabric Pankaj Attri,
Matthew J. Becker		 source
presentation source		 8
PHUSE US 2026 Orchestrating Efficiency: Symphony and Composer in Clinical Reporting Margaret Wishart,
Karma Tarap		 source
presentation source		 6
PHUSE US 2026 QC Without Code: ChatGPT Enterprise for Sponsor Oversight of Outsourced TFLs Jenny Price,
Brad Moore		 source
presentation source		 9
PHUSE US 2026 Real-Time AI-Driven TLF Generation with GAutoTLF Hao Xu,
Tyler Rowsell		 source
presentation source		 14
PHUSE APAC 2026 Integrating R Programming and Generative AI for Prompt-Driven Clinical Trial Data Analysis Vyom Bhatt source
presentation source		 4
PHUSE APAC 2026 Sparc AI: A Native Metadata-Driven Engine for End-to-End Clinical Trial Analysis Anil Mogha,
Ashwin Kumar Nyalakonda,
Gourav Garg		 source
presentation source		 7
PHUSE EU 2025 AI Chatbot in an SCE Shivoy Pandita,
Marc Jantke		 source
presentation source		 8
PHUSE EU 2025 From Mock to Output: Using Generative AI to Write SAS Code for Standards-Based TFLs Kala Shivali,
Bhuvana Venkatappa		 source
presentation source		 16
PHUSE EU 2025 Turning Data into Deliverables: Automating Biometrics from Metadata to TFLs Inderjeet Arora source
presentation source		 9
PHUSE EU 2025 Your Outputs Deserve a Second Set of Eyes - Meet ELARA Mane Sargsyan source
presentation source		 7
MWSUG 2025 Prompt, Program, Submit: Generative AI for Faster SDTM, ADaM, and TLFs Matthew J. Becker,
Pankaj Attri		 source 7
WUSS 2025 Prompt, Program, Submit: Generative AI for Faster SDTM, ADaM, and TLFs Matthew J. Becker source 7
PHUSE US 2025 Expediting the Regulatory Submission Process Through Automated Tools Manish Deole,
Gautham Selvaraj		 source
poster source		 4
PHUSE US 2025 Let the Machine Do the Coding: A SAS-Based Framework for Automating TFL Generation with ARS Metadata Julie Wachara source
presentation source		 9
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 LLMs in Clinical Trials: Overcoming Challenges Related to Patient Confidentiality and Intellectual Property Amit Javkhedkar,
Samip Shah		 source
presentation source		 7
PHUSE EU 2024 Driving Efficiency and Automation in TFL Generation: A Case Study of Establishing an Oncology TFL Library and Analysis Results Metadata Repository [poster] Salil Parab,
Bhavin Busa		 source 1
PHUSE EU 2024 Verify: Facilitating End-to-End Automation of Clinical Validation Processes Using AI Algorithms and Dynamic Collaboration Ilan Carmeli source
presentation source		 3
SESUG 2024 The Essentials Given - Becoming an Expert of Statistical Programming in Pharma & Health Care Industry Hongbo Li source 5
PharmaSUG CN 2024 An end-to-end Digital Platform for TFL Automation - From Metadata to TFL Outputs Yanan Sui,
Jiapeng He,
Mingliang Fan,
Xuejie Zhou,
Lin Jiang,
Tingting Zeng,
Jinling Li		 source 8
PharmaSUG 2024 AI and the Clinical Trial Validation Process - Paving a Rocky Road Steve Ross,
Ilan Carmeli		 source 7
PharmaSUG 2024 Automating the annotation of TLF mocks Using Generative AI Vidya Gopal source 6
PharmaSUG 2024 Multiple Logistic Regression Analysis using Backward Selection Process on Objective Response Data with SAS Girish Kankipati,
Jai Deep Mittapalli		 source 9
PHUSE EU 2023 Leveraging the Analysis Results Standard (ARS): The Cytel PRISM Experience Nicolas Rouillé,
Sebastià Barceló		 source
presentation source		 8
WUSS 2023 Navigating SAS and CDISC Certification with Apprenticeship Sarvar Khamidov source 7
PharmaSUG 2023 Exploring the use of AI-based image recognition and Machine Learning to Improve the Efficiency and Accuracy of TLFs Validation Process. Mor Ram-On,
Ilan Carmeli		 source 5
PharmaSUG 2023 Masters of the Table Universe: Creating Table Shells Consistently and Efficiently Across All Studies Michael Hagendoorn,
Ran Li,
Mimi Vigil,
Shan Yang		 source 15
PHUSE US 2023 Efficient Integration Programming Techniques [poster] Vaibhav Sathe,
Aparna Polisetty		 source 1
PHUSE US 2023 The Role of AI in Handling Clinical Data and Documents (Structured and Unstructured) Farha Feroze source
presentation source		 6
PHUSE EU 2022 Automation in Biostatistics at Novo Nordisk [presentation] Matthew Phelps source
video		 37
PHUSE EU 2022 From Artifact to Metadata: A Divergent Approach to Automation Through Standardisation Nicolas Rouillé,
Camie Britton		 source
presentation source		 4
PHUSE EU 2022 Generating Statistical TFLs Using ML, Deep Learning and NLP Techniques that Provide More Efficiency to the Statistical Programming Process [presentation] Jeyantharkumar Arunachalam source 7
PHUSE EU 2022 How to Avoid Conformance Issues When Metadata Varies Across Parameters [presentation] Caroline Pelckmans,
Oussama Zineddine		 source
video		 24
PharmaSUG 2022 Generating statistical Tables, Listings, and figures using machine learning, deep learning, and NLP techniques that provide more efficiency to the statistical programming process Farha Feroze,
Ilango Ramanujam		 source 3
PharmaSUG 2022 Introduction to Annual Reporting for Beginners! Akshita Gurram,
Mamatha Mada		 source 8
PHUSE EU 2021 The Concept of IoT - The Next Generation of Statistical Computing Platform [presentation] Huadan Li,
Zhiping Yan		 source
video		 20
PharmaSUG 2021 A metadata driven approach to mock TFL generation Anne Petersen,
Cristina Boschini,
Tobias Kröpelin		 source 5
PharmaSUG 2021 Dynamic macro using the convergence dataset to generate the Odds Ratio (OR), Risk Ratio (RR) and Risk Difference (RD) per SOC/PT using Genmod procedure Jagadish Katam source 20
SGF 2021 A SAS Macro for Dynamic Page Breaks Manohar Modem,
Bhavana Bommisetty		 source 14
PHUSE EU 2020 Last Mile Problem to Manage Data Transformation for Analysis & Reporting [presentation] Deven Babre,
Rajaram Venkatesan		 source
video		 19
PharmaSUG 2020 A SAS Macro for Dynamic Assignment of Page Numbers Manohar Modem,
Bhavana Bommisetty		 source 10
PHUSE US 2020 Lab Master List File Makes Easy Clio Wu source
presentation source		 7
PHUSE US 2020 Programmer to SME - Career Game Changer Divya Cetlur,
Ephicacy Lifescience		 source
presentation source		 7
PharmaSUG 2019 Improving the Relationship between Statisticians and Programmers in Clinical Trial Studies Mai Ngo,
Mary Grovesteen,
Vaughn Eason		 source 8
SGF 2019 Make SAS Viya Realize Strategic Data Science - Data Governance, AI Platform and AI SAS Programmer Ryo Kiguchi,
Katsunari Hirano,
Yoshitake Kitanishi		 source 8
PHUSE US 2018 Don't Sweat It! Consider Taking the Risk Out of your Outsourced Deliverables Jennifer True,
Nancy Haeusser		 source
presentation source		 4
PHUSE US 2018 Harnessing the Web to Streamline Statistical Programming Processes Daniel J. Boisvert,
Gordon Schantz		 source
presentation source		 8
PharmaSUG 2018 Family of PARAM***: PARAM, PARAMCD, PARAMN, PARCATy(N), PARAMTYPE Kamleshkumar Patel source 12
PharmaSUG 2017 Metadata of Titles, Footnotes, and Proc Report Details Julius Kirui source 11
PharmaSUG 2017 Sponsor Oversight of CROs Data Management and Biostatistical Abilities Lois Lynn source 7
PharmaSUG 2016 Validation Methods and Strategies for Presentation of Clinical Reports: The Programmers Road Map to Success Vijayata Sanghvi source 5
PharmaSUG 2015 Advantages and Disadvantages of Two Commonly Used CRO Resourcing Models in the Pharmaceutical SAS Programming Environment Ying (Evelyn) Guo,
Mark Matthews		 source 7
PharmaSUG 2015 Lessons Learned from the QC Process in Outsourcing Model Faye Yeh source 5
PharmaSUG 2015 Macro to Read Mock Shells in Excel and Create a Copy in Rich Text Format Rakesh Mucha,
Jeffrey Johnson		 source 11
PHUSE EU 2014 Metadata Design for Analysis and Reporting Gopinath Viswanathan,
Giri Balasubramanian		 source
presentation source		 2
PharmaSUG 2014 Clinical Study Report Review: Statistician's Approach Amita Dalvi source 6
PHUSE EU 2013 Statistics: The Fourth Dimension of a "Statistical Programmer" Gauri Khatu,
Vibhavari Inamadar		 source 12
PharmaSUG 2010 From SAP to ADaM: The Nuts and Bolts Nancy Brucken,
Paul K. Slagle		 9
PHUSE EU 2008 Titles and footnote management from the TLF shell to the final outputs Thierry Jegoux source 10

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