HOME QUICK SEARCH AUTHORS KEYWORDS HELP BUGS

randomization

298 papers and 10 presentations; 289 titles.

Back to keywords

Conference × Year × Title × Authors Extras Pages
PharmaSUG CN 2026 Safety Estimands and Analysis Dataset Design: Advancing Safety Evaluation in Clinical Trials Kun Ren,
Zeyu Guang		 source 11
PharmaSUG 2026 Algorithms to align the distribution of follow-up across independently collected cohorts when comparing time to event endpoints using conventional Kaplan Meier and Cox regression methods. Shankar Srinivasan,
Yvonne Buttkewitz,
Regina Uttenreuther,
Baldeep Chani Talwar,
Manjari Dissanayake		 source 12
PharmaSUG 2026 Construction and Evaluation of External Controls Using Propensity Score Methods Bala Niharika Pillalamarri,
Yayu Li,
Hongbing Jin		 source 14
PharmaSUG 2026 Following Your Footsteps: Maintaining Traceability in ADaM Datasets [presentation] Nancy Brucken,
Karl Miller		 source 59
PharmaSUG 2026 Oncology Solid Tumor Subcutaneous vs Intravenous Late-stage Study Analysis Sumanjali Mangalarapu,
Chuqing Chen,
Anilkumar Anksapur		 source 11
PharmaSUG 2026 Operationalizing Real World Data for External Control Arms: An End to End Framework for Rare Disease and Oncology Trials Anbu Damodaran source 7
PHUSE US 2026 Closing the Gap: Identifying Missing Tumour Assessments in Oncology Studies Ke Xiao source
presentation source		 8
PHUSE US 2026 Demystifying TMLE: A Practical Approach for Clinical Data Scientists Joshua Enxing source
presentation source		 12
PHUSE US 2026 Deriving Phantom Records in BDS Datasets To Support the FDA's Standard Safety Figures for Missing Data Analysis Guowei Wu,
Ruchitbhai Patel		 source
presentation source		 6
PHUSE US 2026 Framework for Incorporating Estimands within the Statistical Analysis Plan Inderjeet Arora source
presentation source		 9
PHUSE US 2026 From Data Cutoffs to Decision-Making: Programming Futility Analysis in Oncology Clinical Trials Mani Mudumba source
presentation source		 6
PHUSE US 2026 Group, Collapse, Repeat: A Dynamic SAS Macro for Stratification Pooling in Oncology Efficacy Analysis Venkatesh Burla source
presentation source		 9
PHUSE US 2026 Handle with Care: Study-Specific Assessment of R Package Risk Shannon Grant source
presentation source		 9
PHUSE US 2026 Implementing AI in Authoring Plain Language Summaries: Insights from a Live Project Experience [presentation] Vidhi Vashisht source 18
PHUSE US 2026 Is There Really Any Benefit to Stratified Randomisation in Practice? Pavankumar Bhagat source
presentation source		 9
PHUSE US 2026 Practical Survival Analysis Techniques for Clinical Trials Using SAS Xue Ding,
Tianchen Xu		 source
presentation source		 10
PHUSE APAC 2026 External Comparator Studies in the Era of Real-World Data Vishal Goriya,
Anshul Sinha,
Gerd Rippin		 source
presentation source		 5
PHUSE APAC 2026 Illustrating treatment switching using Rank preserving structural failure time model in Oncology study Swarupa Changan source
presentation source		 11
PHUSE APAC 2026 Leveraging Real-World Evidence (RWE) in Clinical Trial Design: Statistical Challenges and Opportunities Muhammed Farzan K source
presentation source		 6
PHUSE APAC 2026 Targeted Maximum Likelihood Estimation: A Unified Approach for Confounding and Selection Bias Mahjoob O. Majeed,
Noel S. John		 source
presentation source		 6
PHUSE EU 2025 Blinded & Coordinated: Aligning People and Processes To Prevent Unblinding Monika Bajek,
Dubravka Georgijevic		 source
presentation source		 6
PHUSE EU 2025 Inverse Probability Weighting in Multiple Groups: Balancing Baseline Characteristics and Improving Precision of Average Treatment Effect Estimation Dimitris Karletsos source
presentation source		 11
PHUSE EU 2025 Is There Really Any Benefit to Stratified Randomisation Compared to Simple Randomisation with Covariate Adjustment? Pavankumar Bhagat source
presentation source		 9
PHUSE EU 2025 Leveraging LLM for R Shiny App Code Development: A Case Study with Event Prediction [presentation] Yirong Cao source 15
PHUSE EU 2025 Navigating Experimental Arms in Subtype Studies: Strategies for Precision and Clarity Narek Torosyan,
Edgar Sargsyan		 source
presentation source		 4
PHUSE EU 2025 Poster PP11 Pragmatism Meets Precision,
Andrea Parodi		 source
poster source		 4
SESUG 2025 Preserving the Blind: Cross-Functional Solutions During Study Design Evolution from Blinded Parent to Open-Label Extension Tanmay Khole 5
SESUG 2025 Statistical Programmer's role in Bioresearch Monitoring (BIMO) package creation Sampath Madanu 25
WUSS 2025 PFS2, did you hear about it too? - Understanding the derivation of Progression Free Survival on next-line therapy (PFS2). Yeshashwini Chenna,
Lupetu Ntambwe		 source 11
PharmaSUG 2025 Avoiding the Ouch: Mastering Time to Pain Progression Analysis Kavitha Boinapally,
Sai Krishna Pavan Nakirikanti,
Hank Dennis,
Yang Wang		 source 9
PharmaSUG 2025 Breaking Barriers in Clinical Trials: Insights into Platform Designs Chetankumar Patel source 11
PharmaSUG 2025 Efficacy Endpoints Related to CNS in Oncology Studies within ADaM Lihui Deng,
Kylie Fan		 source 6
PharmaSUG 2025 Implementation of the inverse probability of censoring weighting (IPCW) Model in Oncology Trials Mei Huang,
Shibani Harite,
Haijun Ma,
Linsong (Athena) Zhang		 source 17
PharmaSUG 2025 Roadmap to Efficacy Analysis for Early Phase Oncology studies Dhruv Bansal,
Christiana Hawn,
Chris Kelly		 source 23
PHUSE US 2025 How Unique is it for the Subject Identifier in a Study To Have Multiple Screenings and Enrolments? Anahita Vyas,
Rama Kudaravalli		 source
presentation source		 8
PHUSE US 2025 The Ultimate Randomisation App Chen Li,
Ivo Marek		 source
presentation source		 9
PHUSE EU 2024 Strategies to Improve Understanding of Estimands and Analysis Section of Statistical Analysis Plan Inderjeet Arora source
presentation source		 8
SESUG 2024 Applying Different Data Cutoff Dates for the Analysis of a Study Milestone Guowei Zhao,
Hong Qi		 source 7
SESUG 2024 Using PROC PSMATCH for Assembling Parallel Test Forms Tsung-Hsun Tsai,
Yung-Chen Hsu		 source 11
PharmaSUG 2024 ADaM Design for Prostate Cancer Efficacy Endpoints Based on PCWG3 Lihui Deng,
Kylie Fan,
Jia Li		 source 10
PharmaSUG 2024 Developing Web Apps in SAS Visual Analytics Jim Box,
Samiul Haque		 source 9
PharmaSUG 2024 How to generate a submission ready ADaM for complex data Yilan Xu,
Hu Qu,
Tina Wu		 source 8
PharmaSUG 2024 Oncology ADaM Datasets Creation Using R Programming: A Comprehensive Approach Ishwar Chouhan source 29
PharmaSUG 2024 Programming Considerations in Deriving Progression-Free Survival on Next-Line Therapy (PFS2) Alec McConnell,
Yun Peng		 source 5
PHUSE US 2024 Are Platform Studies the Need of the Hour for Oncology Trials? Vidya Muthukumar source
presentation source		 2
PHUSE US 2024 Confirmation and Cancellation - Best Strategies for Data Collection and Handling within Solid Tumor Studies Using RECIST 1.1 and iRECIST Guidelines Sherry Ye,
Abraham Yeh		 source
presentation source		 10
PHUSE US 2024 Eliminating Duplicative Maintenance of Statistical Programs In Parallel Analyses Using a Single Control File Kavitha Boinapally source
poster source		 4
PHUSE US 2024 InnoRand - Innovative Randomisation Yinfei Wu,
Ivo Marek		 source
presentation source		 4
PHUSE EU 2023 Efficacy Endpoints in Adjuvant and Neo-Adjuvant Oncology Trials Yuliia (Piddubna) Cherednichenko,
Anna Amosova		 source
presentation source		 15
PHUSE EU 2023 Interaction with the FDA During and After a Type C Meeting, From the Perspective of a Statistical Programmer Dieter Hartz source
presentation source		 5
WUSS 2023 Visualizing Insights, Empowering Discoveries: SAS Viya Unleashed in Life Science Analytics Matthew J. Becker source 13
SESUG 2023 BLINDED STUDIES AND CHALLENGES Jalender Reddy Musku,
Srinivas Tiyyagura		 source 5
SESUG 2023 SAS Macro for Randomization-Based Methods for Covariance and Stratified Adjustment of Win Ratios and Win Odds for Ordinal Outcomes Elaine Kowalewski,
Ann Marie K. Weideman,
Gary G. Koch		 source 11
PharmaSUG 2023 A model for sponsors to support independent operation of IDMCs in clinical trials. Bhanu Bayatapalli,
Yiyi Chen		 source 8
PharmaSUG 2023 ADaM Design and Programming Standardization for Oncology Efficacy Endpoints Based on RECIST 1.1 Ellen (Zihui) Lin,
Matt Ness,
Yinghui Wang		 source 9
PharmaSUG 2023 Admiralonco - The Cross-Company R Package for Oncology Admirers Neharika Sharma,
Matthew Marino		 source 25
PharmaSUG 2023 Data Doesn't Lie - Real Time Monitoring and Projecting on Clinical Trial Enrollment Progression Wenjun He source 5
PharmaSUG 2023 Shifting the drug development paradigm with Adaptive Design and master protocol Aman Bahl source 16
PharmaSUG 2023 Survival Methods for Crossover in Oncology Trials Brian Mosier source 18
PHUSE EU 2022 Adaptive Design - Better Bend Than Break [presentation] Albin John,
Athira Sudhakar		 source 15
SESUG 2022 A SAS Macro to Calculate Blinding Index in Clinical Trials: %blinding_index, an application of PROC IML Jingwei Wu,
Di Zhu		 source 7
PharmaSUG 2022 Automation using SAS Makes it Easy to Monitor Dynamic Data in Clinical Trial Wenjun He source 9
PharmaSUG 2022 Derivation of Efficacy Endpoints by iRECIST Criteria: A Practical Approach Ilya Krivelevich,
Lei Gao,
Richard Kennan		 source 9
PharmaSUG 2022 Following Your Footsteps: Maintaining Traceability in ADaM Datasets Nancy Brucken,
Karl Miller		 source 58
PharmaSUG 2022 How to create SDTM SAS transport datasets using Python Kevin Lee source 8
PharmaSUG 2022 Merging Pharmacokinetic (PK) Sample Collection and Dosing Information for PK Analysis based on Study Design and lessons learned Durga Kalyani Paturi source 11
PharmaSUG 2022 Quality Control With External Code Bill Coar source 7
PharmaSUG 2022 Quality Gates: An Overview from Clinical SAS Programming Perspective Nagadip Rao,
Pavan Jupelli		 source 10
PHUSE US 2022 Real-time Interactive Dashboard for Clinical Trials [presentation] Kris Liu source 9
PHUSE US 2022 Safety First, Agility Comes: Challenges and Solutions to Fulfilling IND Safety Requirements Hong Wang,
Ke Xiao		 source
presentation source		 7
PHUSE EU 2021 Statistical Training for Health Technology Assessment Statistical Programmers Anastasia Kokoreva,
William Malbecq		 source
video
presentation source		 7
SESUG 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source 21
PharmaSUG 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source 21
PharmaSUG 2021 No Black Boxes: An Utterly Transparent Batch Randomization System in R Dennis Sweitzer source 12
PharmaSUG 2021 Survival Statistics with PROC LIFETEST and PROC PHREG: Pitfall-Avoiding Survival Lessons for Programmers Yun (Julie) Zhuo source 10
PharmaSUG 2021 Willing to Play with William-s Design: An Exemplification of Randomization Schedule Generation using SAS Abhyuday Chanda source 12
SGF 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source
presentation		 20
SGF 2021 Modeling Causal Effects Using SAS/STAT John Amrhein,
McDougall Scientific		 source 21
PHUSE EU 2020 ADaM is Back: The New ADaM IG 1.2 and Beyond Angelo Tinazzi source
video		 8
PHUSE EU 2020 Analysis Used in RBM [presentation] Aminul Islam,
Shafi Chowdhury		 source
video		 8
PHUSE EU 2020 Brace Yourself for Smooth Unblinding [presentation] Vladyslav Khudov source
video		 26
PHUSE EU 2020 SUPPQUAL Datasets: The Good, the Bad and the Ugly [presentation] Sergiy Sirichenko source
video		 40
SESUG 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 13
SGF 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 13
PharmaSUG 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 14
PharmaSUG 2020 End-to-end Prostate-Specific Antigen (PSA) Analysis in Clinical Trials: From Mock-ups to ADPSA Joy Zeng,
Varaprasad Ilapogu,
Xinping Cindy Wu		 source 12
PharmaSUG 2020 Overcoming Pitfalls of DS: Shackling the 'Elephant in the room' Soumya Rajesh,
Michael Wise		 source 9
PharmaSUG 2020 Principal Stratum strategy for handle intercurrent events: a causal estimand to avoid biased estimates Andrea Nizzardo,
Giovanni Marino Merlo,
Simona Scartoni		 source 13
PharmaSUG 2020 Trial Sets in Human Clinical Trials Fred Wood source 13
PharmaSUG 2020 Untangling the Subject Elements Domain Christine McNichol source 8
PharmaSUG 2020 Using SAS Simulations to determine appropriate Block Size for Subject Randomization Lists Kevin Venner,
Jennifer Ross,
Kyle Huber,
Noelle Sassany,
Graham Nicholls		 source 10
PHUSE US 2020 Adverse Event and Treatment Emergent Approaches in the Industry Aimee Basile source
poster source		 11
PHUSE US 2020 Be the Master for a Master Protocol Study Ke Xiao source
poster source		 8
PHUSE US 2020 Confusing Data Validation Rules Explained, Part 2 Michael Beers,
Blue Bell		 source 6
PHUSE US 2020 Design and Conduct of Clinical Trials: Past Present and Future. New Innovative Technology Methods George Obsekov source
presentation source		 12
PHUSE US 2020 Great!! Not So Great ... How to Capture it All in an Effective Lessons Learned Discussion Monali Khanna source
presentation source		 7
PHUSE US 2020 Let Us Trace Back to the Beginning from the End Madhusudhan Reddy Papasani,
Swathi Kotla		 source
presentation source		 12
PHUSE US 2020 Untangling the Subject Elements Domain Christine McNichol source
presentation source		 7
PHUSE EU 2019 Analysis of composite endpoints using the Win-Ratio method in SAS Murat Ipek source
presentation source		 8
PHUSE EU 2019 Sub-population Detection Using Graph-based Machine Learning Sergey Glushakov,
Kostiantyn Drach		 source 28
PharmaSUG 2019 Camouflage your Clinical Trial with Machine Learning and AI Ajith Baby Sadasivan,
Limna Salim,
Akhil Vijayan,
Anoop Ambika		 source 10
PharmaSUG 2019 Enhancing Randomization Methodology Decision-Making with SAS Simulations Kevin Venner,
Jennifer Ross,
Kyle Huber,
Graham Nicholls		 source 15
PharmaSUG 2019 Forewarned is forearmed or how to deal with ADSL issues Anastasiia Oparii source 16
PharmaSUG 2019 Jump Start your Oncology knowledge Xiaoyin Zhong,
Feng Liu		 source 9
PharmaSUG 2019 Progression-Free Survival (PFS) Analysis in Solid Tumor Clinical Studies Na Li source 14
PharmaSUG 2019 Useful SAS techniques in Efficacy Analysis for Oncology studies Joy Zeng source 21
SGF 2019 The Benefits of Keeping It Casual: Investigating Stress-Mediated Health Outcomes with PROC CAUSALMED Daniel Muzyka,
Matthew Lypka		 source 19
PHUSE US 2019 A SAS Macro for Variable Ratio Treatment vs. Placebo in Propensity Score Matching Punjita Baranwal,
Prashant Kulkarni		 source
presentation source		 13
PHUSE US 2019 Misuse and Misapplication in Statistical Data Analysis - A Topic that Never Goes Out of Style Corinna Miede source
presentation source		 12
PHUSE EU 2018 Practical Benefits of EC: Building a Comprehensive Exposure Story Zegher Vereecke,
Donald Benoot		 source
presentation source		 5
PHUSE US 2018 ADaM Intermediate Dataset: How to Improve Your Analysis Traceability Angelo Tinazzi,
Ashish Aggarwal		 source
presentation source		 12
PHUSE US 2018 How do I have ONE Interpretation of CDISC for all my Studies? Mor Meyerovich source
presentation source		 5
PHUSE US 2018 Practical Benefits of EC: Building a Comprehensive Exposure Story Donald Benoot,
Zegher Vereecke		 source 5
PHUSE US 2018 Subgroup Explorer and AdEPro - The Full Picture on Clinical Trial Data [presentation] Madhurima Majumder,
Erya Huang		 source 20
PharmaSUG 2018 ADINTDT and ADTTE for Survival Sweep in Oncology Studies Christine S Teng,
Wenyu Hu		 source 6
PharmaSUG 2018 An Efficient Tool for Clinical Data Check Chao Su source 8
PharmaSUG 2018 Analysis of Oncology Studies for Programmers and Statisticians Kevin Lee source 7
PharmaSUG 2018 Building Simulated CRF Data for Study Mock-Ups Ashley Kesler,
Hisham Madi,
Jonathan W. Duggins,
James E. Blum		 source 10
PharmaSUG 2018 Data Management and CDISC Formatting for Transdermal Patches Lois Lynn source 11
PharmaSUG 2018 Implementing the Rank-Preserving Structural Failure Time Model in SAS and R Bradford J. Danner,
Indrani Sarkar		 source 8
PharmaSUG 2018 Sense and Censorability: Learn censoring techniques with ADTTE for your survival Shilpakala Vasudevan source 12
SGF 2018 Robust Tuning for Machine Learning Alex Glushkovsky source 22
SESUG 2017 A Macro for Generating the Adverse Events Summary for ClinicalTrials.gov Andrew Moseby,
Maya Barton		 source 7
SESUG 2017 Statistician's secret weapon: 20 ways of detecting raw data issues Lixiang Larry Liu source 8
SESUG 2017 Timing Variables In Clinical Trials: Avoiding Common Mistakes And Dealing With Unforeseen Issues Gregory Weller source 4
SCSUG 2017 Risk Based Monitoring (RBM) with SAS Visual Analytics Prasoon Sangwan,
Piyush K. Singh,
Faraz Khan		 source 10
PHUSE EU 2017 Assessing New CFDA Requirements on Data Integrity Using Risk-Based Monitoring and Enrollment Patterns Wenjun Bao,
Richard C. Zink,
Geoffrey A. Mann		 source
presentation source		 13
PHUSE EU 2017 Decrypting SDTM Trial Design Datasets for Complex Study Designs Charumathy Sreeraman source
presentation source		 14
PHUSE EU 2017 FlexRandomizer: Easy Randomisation in Real Time Priyadarshan Shinde,
Mangesh Kulkarni		 source
presentation source		 12
PHUSE EU 2017 Managing Study Workflow Using the Resource Description Framework (RDF) Armando Oliva source 7
MWSUG 2017 Waterfall Plots in Oncology Studies in the Case of Multi-Arms Design Ilya Krivelevich,
Kalgi Mody,
Simon Lin		 15
PharmaSUG 2017 Data Monitoring Committee Report Programming: Considering a Risk-Based Approach to Quality Control Amber Randall,
Bill Coar		 source 9
PharmaSUG 2017 Overview and Application of the HCV Vertical Resistance Analysis Template Yan Xie source 5
PharmaSUG 2017 SAS Macro for Derivation of Best Overall Response per RECIST v. 1.1 Bob Zhong,
Jiangfan Li,
Hong Xie,
Peter De Porre,
Kenneth Maahs,
Kyounghwa Bae		 source 9
PharmaSUG 2017 Statistician's secret weapon: 20 ways of detecting raw data issues Lixiang Larry Liu source 8
PharmaSUG 2017 Visualizing Enrollment Over Time Laura Gruetzner source 11
SGF 2017 Differentiate Effects from the Noise of Promotional Marketing Campaigns Alex Glushkovsky,
Matthew Fabian		 source 9
SGF 2017 SAS: A Unifying Tool That Manages Hospital and Research Pharmacy Data and Reporting Robert MacArthur,
Arman Altincatal		 source
pdf		 8
SESUG 2016 In the Pursuit of Balanced Groups: A SAS Macro for an Adaptive Randomization Test Tyler Hicks,
Jeffrey D. Kromrey		 source 9
SESUG 2016 SDTM What? ADaM Who? A Programmer's Introduction to CDISC Venita DePuy source 9
PHUSE EU 2016 EnForeSys - An Advanced Patient Enrolment Forecaster with Monte Carlo Simulations Ritika Yadav,
Swechhya Bista		 source
presentation source		 12
WUSS 2016 Selection Bias: How Can Propensity Score Utilization Help Control For It? Deanna Naomi Schreiber-Gregory source 13
WUSS 2016 Why and What Standards for Oncology Studies (Solid Tumor, Lymphoma and Leukemia)? Kevin Lee source 7
PharmaSUG 2016 Creating Time to Event ADaM Dataset for a Complex Efficacy Endpoint in Multiple Sclerosis Therapeutic Area Ittai Rambach source 8
PharmaSUG 2016 Scrambled Data - A Population PK/PD Programming Solution Sharmeen Reza source 8
PharmaSUG 2016 The Standard for the Exchange of Nonclinical Data (SEND): History, Basics, and Comparisons with Clinical Data Fred Wood source 10
MWSUG 2015 Using SAS for the Longitudinal Analysis of Difference Scores Brandy R. Sinco,
Edith Kieffer,
Michael S. Spencer,
Gloria Palmisano,
Gretchen Piatt,
Michele Heisler		 14
SESUG 2015 Multilevel Randomization Marina Komaroff,
Lois Lynn		 source 10
PharmaSUG CN 2015 Reading and Resolving OpenCIDSC Message [presentation] Yu Pang 23
PharmaSUG 2015 Analysis Methods for a Sequential Parallel Comparison Design (SPCD) Harry Haber,
James Zuazo,
Linda LaMoreaux		 source 11
PharmaSUG 2015 Missing Data For Repeated Measures: Single Imputation VS Multiple Imputation Giulia Tonini,
Simona Scartoni,
Camilla Paoli,
Andrea Nizzardo,
Angela Capriati		 source 10
PharmaSUG 2015 Multilevel Randomization Marina Komaroff,
Lois Lynn		 source 10
PharmaSUG 2015 Team-work and Forensic Programming: Essential Foundations of Indestructible Projects Brian P. Fairfield-Carter source 14
PharmaSUG 2015 Two different use cases to obtain best responses using RECIST 1.1: SDTM and ADaM Kevin Lee,
Vikash Jain		 source 17
SGF 2015 Adaptive Randomization Using PROC MCMC Qianyi Huang,
John Amrhein		 source 13
SGF 2015 Experimental Approaches to Marketing and Pricing Research Max Friedauer,
Jason Greenfield,
Yuhan Jia,
Joseph Thurman		 source 22
SGF 2015 Permit Me to Permute: A Basic Introduction to Permutation Tests with SAS/IML Software John Vickery source 15
SGF 2015 Random vs. Fixed Effects: Which Technique More Effectively Addresses Selection Bias in Observational Studies Jessica Navratil-Strawn,
Stephen Hartley,
Ronald J. Ozminkowski		 source
zip		 8
SGF 2015 Using SAS to Deliver Web Content Personalization Using Cloud-Based Clickstream Data and On-premises Customer Data Mahesh Subramanian,
Suneel Grover		 source 24
SESUG 2014 A Comprehensive Automated Data Management System For Clinical Trials Heather F. Eng,
Jason A. Lyons,
Theresa M. Sax		 source 12
SESUG 2014 SDTM What? ADaM Who? A Programmer's Introduction to CDISC Venita DePuy source 9
PHUSE EU 2014 Checking Symmetry in Predefined Time Assessments Antonio Nieto,
José Luis Jiménez Moro		 source
presentation source		 9
PHUSE EU 2014 Data Challenges in Adaptive Trials Claudio Garutti source
presentation source		 5
MWSUG 2014 A Comprehensive Automated Data Management System For Clinical Trials Heather F. Eng,
Jason A. Lyons,
Theresa M. Sax		 12
MWSUG 2014 Comparing regression, propensity matching and coarsened exact matching in healthcare observational studies using SAS: An example from the Medical Expenditure Panel Survey (MEPS) Douglas Thompson 16
MWSUG 2014 Using The Delta Method With Proc Mixed To Generate Means And Confidence Intervals From A Linear Mixed Model On The Original Scale, When The Analysis Is Done On The Log Scale Brandy R. Sinco,
Edith Kieffer,
Michael S. Spencer		 11
PharmaSUG 2014 An ADaM Interim Dataset for Time-to-Event Analysis Needs Tom Santopoli,
Kimberly Minkalis,
Sandra Minjoe		 source 9
PharmaSUG 2014 Clinical Study Report Review: Statistician's Approach Amita Dalvi source 6
PharmaSUG 2014 Defensive Programming, Avoiding the Big Mistakes David W. Franklin source 11
PharmaSUG 2014 Methodology for Non-Randomized Clinical Trials: Propensity Score Analysis Dan Conroy source 5
PharmaSUG 2014 Using the Power of SAS SQL Jessica Wang source 14
SGF 2014 Adjusting Clustering: Minimize Your Suffering! Brenda Beaty,
L. Miriam Dickinson		 source 9
SGF 2014 Analyzing Data from Experiments in Which the Treatment Groups Have Different Hierarchical Structures Sharon Lohr,
Peter Schochet		 source 12
SESUG 2013 A SAS Macro for Finding Optimal k-Means Clustering in One Dimension with Size Constraints Fengjiao Hu source 5
PHUSE EU 2013 Define-XML v2 - What's New Lex Jansen source
presentation source		 15
PHUSE EU 2013 Efficacy Endpoints in Oncology Angelo Tinazzi source 9
MWSUG 2013 Using SAS in Clinical Programming Project Management from Excel Spreadsheets Jean Crain 13
PharmaSUG 2013 ADaM Datasets for Graphs Kevin Lee,
Chris Holland		 source 15
PharmaSUG 2013 Adjusted proportion difference and confidence interval in stratified randomized trials Yeonhee Kim,
Seunghyun Won		 source 8
PharmaSUG 2013 Doctoring Your Clinical Trial with Adaptive Randomization: SAS Macros to Perform Adaptive Randomization Jenna Colavincenzo source 9
PharmaSUG 2013 SAS Programmer to Clinical SAS Programmer Gayatri Karkera,
Nehan Mohan		 source 7
SGF 2013 Doctoring Your Clinical Trial with Adaptive Randomization: SAS Macros to Perform Adaptive Randomization Jenna Colavincenzo source 9
NESUG 2012 A Visual Approach to Monitoring Case Report Form Submission During Clinical Trials Rebecca Horney,
Karen Jones,
Annette Wiseman		 7
PHUSE EU 2012 "Bertha.sas" - User Friendly Ways to get Traceability Elena Glathe source
presentation source		 28
PHUSE EU 2012 Improving Data Management with Systems Integration and Next Generation Data Flow Richard Davies source
presentation source		 4
SESUG 2012 A Randomization Test SAS/IML Program for Making Treatment Effects Inferences for Extensions and Variations of ABAB Single-Case Experimental Designs Patricia Rodríguez de Gil,
John M. Ferron		 source 8
SESUG 2012 A Visual Approach to Monitoring Case Report Form Submission During Clinical Trials Rebecca Horney,
Perry Point		 source 7
WUSS 2012 Up To Speed With Categorical Data Analysis Maura E. Stokes source 25
PharmaSUG 2012 A SAS Tool to Allocate and Randomize Samples to Illumina Microarray Chips Huanying Qin,
Greg Stanek,
Derek Blankenship		 source 6
SESUG 2011 Permutated-block randomization with varying block sizes using SAS Proc Plan Lei Li source 6
WUSS 2011 SAS Program as a Backup Tool for an Adaptive Randomization System Yajie Wang source 9
WUSS 2011 Up To Speed With Categorical Data Analysis Maura E. Stokes source 25
PHUSE EU 2011 Access Control Execution Systems (ACES) Shailesh Kulthe,
Eric Silva		 source 10
MWSUG 2011 Using a Double DOW Loop to Compute Progression Free Survival Matthew Nizol 9
NESUG 2011 A Practical Method to Generate Desirable Group Sizes by Performing Adaptive Randomization Feifei Yang 5
NESUG 2011 Adaptive Randomization: Institutional Balancing Using SAS Macro Rita Tsang,
Katherine Kacena		 12
PharmaSUG 2011 Using the ADaM Basic Data Structure for Survival Analysis Nancy Brucken,
Sandra Minjoe,
Mario Widel		 source 5
SGF 2011 Randomizing Small Strata Groups: Balancing Baseline Population Measures While Staying True to a Chosen Allocation Strategy, an Alternative to PROC SURVEYSELECT Gregg M. Oginz source 12
SGF 2011 Up To Speed With Categorical Data Analysis Gary G. Koch,
Maura E. Stokes		 source 25
NESUG 2010 Delivering a Simple Randomization list using RTF MinJung Yoon 5
NESUG 2010 Macro for Computing Complier-Average Causal Effect of Treatment (CACE) Using Instrumental Variable Method Patricia R. Houck,
Sati Mazumdar,
Benoit H. Mulsant		 3
NESUG 2010 Using SAS for Design of Experiments: An Unbalanced Incomplete Block Design Burhan Ogut 7
WUSS 2010 Blocked Randomization with Randomly Selected Block Sizes: A Flexible SAS Macro Jimmy Thomas Efird source 3
SESUG 2010 Proc Report Data = Subject.Event_Chronology; Christina Carty,
Elizabeth R. Spence		 source 12
PharmaSUG 2010 %disposition_plot: A Macro Application for Clinical Study Disposition Analysis Yao Huang 8
SGF 2010 The Best of Both Worlds: Designing Experiments in the JMP and SAS Environment Jose G. Ramirez source 12
SESUG 2009 SAS and Clinical IVRS: Beyond Schedule Creation Gayle Flynn source 10
PHUSE EU 2009 Timezone correction of data John van Bemmelen source 3
WUSS 2009 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell source 18
PharmaSUG 2009 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell 18
PharmaSUG 2009 SAS 1-liners Stephen Hunt 6
SGF 2009 Designing Experiments for the Process George A. Milliken source 27
SGF 2009 Designing Multi-Step Fractional Factorial Split-Plots: A Combined JMP and SAS User Application Jose G. Ramirez,
Jon T. Weisz		 source 11
WUSS 2008 Using Propensity Scores To Adjust For Treatment Selection Bias R. Scott Leslie source 7
PHUSE EU 2008 A gentle introduction to Survival Analysis Martin Scottt,
Angelo Tinazzi		 source 13
MWSUG 2008 Creating Macros for Survival Data in Oncology Study Jagannath Ghosh 8
WUSS 2007 A SAS Algorithm for Block Randomization Jimmy Thomas Efird,
Miki Hong,
Pollie Bith-Melander,
Chieko Kimata,
Caroline Jiang,
Kathleen Baker		 source 4
WUSS 2007 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell source 18
PNWSUG 2007 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell 18
PharmaSUG 2007 A Review of Propensity Score Application in Healthcare Outcome and Epidemiology Guiping Yang 24
PharmaSUG 2007 Randomization Schedules - Made Easy Michael Stout 10
SGF 2007 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell source 18
SGF 2007 Local and Global Optimal Propensity Score Matching Marcelo Coca-Perraillon source 9
SGF 2007 Split and Conquer! Using SAS/QC to Design Quality into Complex Manufacturing Processes Jose G. Tobias,
Randall D. Ramirez		 source 25
PharmaSUG 2006 A Case of Online Data Processing and Statistical Analysis via SAS/IntrNet Sijian Zhang 8
PharmaSUG 2006 Patient Profile: A Menu-Driven System Adel Fahmy 12
PharmaSUG 2006 Randomization in Clinical Trial Studies David Shen,
Zaizai Lu		 8
PharmaSUG 2006 Sample Size Calculation and Timeline Estimate for Progression-Free Survival Chung-Kuei Chang 10
SUGI 2006 Using the SAS System for Experimental Designs for Multi-Component Interventions in Medicine Heather G. Allore,
Peter Peduzzi,
Ling Han,
Mary Tinetti		 source 7
PNWSUG 2005 Extreme Graphics Make Over: Using SAS/GRAPH to Get the Graphical Output You Need Miriam G. Cisternas,
Arthur L. Carpenter		 16
PHUSE EU 2005 Splendida fugilente oculi, e.g. dummy randomization lists to more than designed patient profiles Magnus Mengelbier source 5
PHUSE EU 2005 The Future of CDISC Becky Kush source 50
WUSS 2005 Extreme Graphics Make Over: Using SAS/GRAPH to Get the Graphical Output You Need Miriam G. Cisternas,
Arthur L. Carpenter		 source 16
NESUG 2005 Introduction to Experimental Design using SAS Jonas V. Bilenas 13
PharmaSUG 2005 A Dynamic SAS/IntrNet Example with Help of ASP and JavaScript: Online Clinical Trial Randomization List Generator Sijian Zhang,
Mark Carpenter,
Michael Windle		 6
PharmaSUG 2005 Analyze Group-randomized Trial Data with Binary Outcome Using Weighted Paired t-test: A SAS Macro Don (Dongguang) Li 5
PharmaSUG 2005 Create Your Customized Case Report Form (CRF) Tracking System Tikiri B. Karunasundera 9
SUGI 2005 Extreme Graphics Make Over: Using SAS/GRAPH to Get the Graphical Output You Need Miriam G. Cisternas,
Arthur L. Carpenter		 source 16
NESUG 2004 Minimization: Testing a Dynamic Randomizaton Algorithm Donna E. Levy,
Emily Blood		 15
MWSUG 2004 The Randomization Test Procedure: Testing for a Non-Zero Quadratic Effect Ling Huang,
Paul W. Johnson		 8
PharmaSUG 2004 Clinical Trial Randomization Design: Blocked Randomized Response Adaptive Method K.Y. Trudy Poon 4
SUGI 2004 A SAS Macro for Generating Randomization Lists in Clinical Trials Using Permuted Blocks Randomization Mazen Abdellatif source 12
SUGI 2004 Some Statistical Strategies for Analyzing Confirmatory Studies Involving One or More Occurrences of Primary Events Gary G. Koch,
Maura E. Stokes		 source 21
WUSS 2003 Clinical Trial Randomization Design: Blocked Randomized Response Adaptive Method K.Y. Trudy Poon,
Che-Chin Lie		 source 4
PharmaSUG 2003 Implementing Constrained or Balanced-Across-the-Centers Randomization with SAS v8 Procedure PLAN Cindy Q. Song,
Olga M. Kuznetsova		 8
PharmaSUG 2003 Sneak Peek: Previewing Blinded Study Results Amy Rhoads Underwood 4
SUGI 2003 Multilevel Designs and Their Analyses George A. Milliken source
SUGI 2003 Using the SAS System to Construct and Operate Control Charts with Randomized Control Limits Peter S. Wludyka source 7
MWSUG 2002 Using SAS System to Geneerate Customized Kaplan-Meieer Curves and its Applciation to Presentation in Clinical Research Daojun Mo,
Yiyong Fu		 14
NESUG 2002 An Animated Guide: Matching Test & Control Subjects Russell M. Lavery,
Glenn Abrahamsen		 21
SESUG 2002 A GUI System for the Generation of Clinical Trial Randomization Plans for Collaborative Multi-institutional Clinical Trials Sijian Zhang,
Mark Carpenter,
John W. Gnann		 source 3
WUSS 2002 A Randomization-Test Wrapper for SAS PROCs Christopher F. Ake,
David L. Cassell		 source 4
SUGI 2002 A Randomization-Test Wrapper for SAS PROCs David L. Cassell source 4
SUGI 2002 Generating Randomization Schedules Using SAS Programming Chunqin Deng,
Julia C. Graz		 source 6
SUGI 2002 Using SAS to Perform Robust I-Sample Analysis of Means Type Randomization Tests for Variances for Unbalanced Designs Peter S. Wludyka,
Ping Sa		 source 7
NESUG 2001 Recursion? SAS? Let.s Fake It! Stephen J. Rhoades 6
SCSUG 2001 Creating Visit Specific CRF Checklists for a Longitudinal Study Emily A. Mixon,
Karen B. Fowler		 4
PharmaSUG 2001 Leveraging Existing SAS Programs for Wireless Delivery Deborah A. Testa,
Russell Holmes		 3
SUGI 2001 A SAS Macro to Isolate All Date Values from a Data Library into a SAS Dataset Michael L. White source 2
SUGI 2001 A SAS Program to Perform Adaptive Randomization Carey G. Smoak,
Jin-Sying Lin		 source 2
SUGI 2001 Wireless SAS Applications for Tracking Clinical Research Deborah A. Testa,
Russell Holmes		 source 3
SESUG 2000 Using SAS to Perform Robust I-Sample Analysis of Means Type Randomization Tests for Variances Peter S. Wludyka source 6
SUGI 2000 A Randomization Test: Somatic Cell Count Slope Comparisons of 55 California Dairies Paul W. Johnson source 4
SUGI 2000 Dynamic Dataset Selection and Project Management using SAS 6.12 and the Windows NT 4.0 File System Matthew T. Downs,
Heidi M. Christ-Schmidt		 source 5
SUGI 2000 Robust Effect Size Estimates and Meta-Analytic Tests of Homogeneity Kristine Y. Hogarty,
Jeffrey D. Kromrey		 source 6
SESUG 1999 Using SAS/AF to Create Applications for the Administrative Aspects of Data Management in Clinical Trials Emily A. Mixon,
Karen B. Fowler,
Felicita David		 source 4
SUGI 1999 A SAS Module/Macro Program for Computing Statistical Power Curves for Detecting Treatment Effect David Olaleye source 6
SUGI 1999 Using the Mixed Procedure to Analyze a Fractional Factorial Split-Plot Experiment Jon M. Lindenauer source 4
SESUG 1998 A SAS Macro Program to Perform Permutation Testing for Group*Time Interaction in an Unbalanced Repeated-Measures Analysis of Variance Mark S. Litaker,
Bernard Gutin		 5
SEUGI 1998 PROC TABULATE in a real WinWord table: a freeware macro Roger Calvez source 6
NESUG 1997 Survival Analysis: Non-Traditional Applications for Non-Statisticians Margaret Ann Goetz 5
SUGI 1997 Building an Online Information Processing System Using SAS Software and a UNIX Shell Script Annie S. Guo,
Long Ngo		 source 6
WUSS 1996 SAS PROC MIXED to Fit Growth Curve Models: Application to a Longitudinal Group Randomization Study Biao Wm. Lu,
Sandra Wilson,
David Latini,
David Dayton		 source 6
SUGI 1996 From Report and Analysis Plan to SAS Report Programs - The Mass Production of Report Skeleton Programs Sam X. Ye source 7
SUGI 1996 Using PROC PLAN for Randomization Assignments Miriam W. Rosenblatt source 5
MWSUG 1995 Randomization and Analysis of Body Weights in Rodent Toxicity Studies Jen-Fue Maa 6
MWSUG 1995 Using PROC PLAN for Randomization Assignments Miriam W. Rosenblatt 6
NESUG 1995 When Chi-Square Does Not Hold: How to Analyze Tables With Low Cell Frequencies With SAS Lothar Tremmel 9
SUGI 1995 Maintaining Blinding in the Presence of Blocking and Identifiable Treatments Sharon Barker source 3
MWSUG 1994 SAS Randomization Techniques for Clinical Trials Linda M. Quinn 6
SUGI 1994 SAS Software Techniques in Longitudinal Studies of Health Outcomes and Costs Julia Hecht,
Jenifer Ehreth,
Mary Durham,
David Grembowski,
Joseph Picciano		 source 6
SUGI 1994 Using SAS/FSP to Develop an On-line Randomization System for Multicenter Clinical Trials Margaret Lee,
Dorothea Collins		 source 6
WUSS 1993 Random Sampling Without Replacement Hong Zhou,
Patrick Romano		 source 2
SEUGI 1993 Support of Statistical Consulting Using SAS Software Hans-Peter Altenburg source 11
SUGI 1993 Administering a Large, Long-Term Clinical Trial Requiring a Hierarchical Database Using the SAS System, Release 6.07 Mitchell Snyder,
Mark Goldberg,
Michal Benderly		 source 6
SUGI 1993 Analysis of Split-Plot Experiments Using SAS Software Stephen P. Jones,
Monica Y. Chi		 source 6
SUGI 1992 A SAS Program to Create Output Files Used for a Randomization Code Distribution Procedure Joseph M. O'Connor,
Ute E. Schwiderski,
Joseph J. Andary		 source 5
SUGI 1992 A SAS Window Application for Generating Follow-up Clinic Schedules and Monitoring Accrual in a VA Cooperative Study Joan Derrico source 6
SUGI 1992 Blocked Randomized Assignment of Treatments to Patients in Clinical Trials Maria A. Deloria,
George F. Reed		 source 6
SUGI 1992 Essential SAS Software for Engineers William D. Kahn,
Diccon R. E. Bancroft		 source 10
SUGI 1992 Use of SAS/AF and SAS/FSP Software to Integrate a Payment System for Patient Information in a Large Multi-Center Clinical Trial Dorothea Collins,
Margaret Lee		 source 6
SUGI 1991 Experiment Design in Engineering Design and Development Douglas C. Montgomery source 10
SUGI 1991 The ETRANK Procedure: Procedures for Analyzing Incomplete Repeated Measures Trials with Treatment Related Withdrawals Richard Entsuah,
Edmond Williams		 source 7
SUGI 1989 A Robust Code for Producing Stratified, Blocked Randomizations Utilizing VMS' Macro Facilities and the SET Statement with the POINT Option David W. Main source 4
SUGI 1989 A SAS/IML Program for Randomization Analysis of Dose Response Curves Dean E. Nelson source 4
SUGI 1989 Field Reliability Assessment Using the Proportional Hazards Model Stephen Freitas,
Raymond V. Spring		 source 5
SUGI 1988 CADAE: Computer-assisted Design and Analysis of Response Surface Experiments Mark J. Levine source 4
SUGI 1988 SPAM: Spatial Autocorrelation Modeling Andrew J. R. Gillespie source 5
SUGI 1986 Using SAS Software in the Design and Analysis of Two-Level Fractional Factorial Experiments Joanne R. Wendelberger source 6
SUGI 1985 A Monte Carlo Comparison of Extended Mantel-Haenszel Procedures Daniel W. Griffin source 6
SUGI 1984 On Generating Statistics, Formatting Results and Efficiency Ingrid A. Amara,
Gary G. Koch		 source 6
SUGI 1984 SEQURAN-An Interactive Sequential Randomization System for Clinical Trials Estelle M. Fondy,
Jeannette Y. Lee		 source 6
SUGI 1983 Overview of Categorical Data Analysis Methods Ingrid A. Amara,
Susan Atkinson,
Gary G. Koch,
William M. Stanish		 source 10
SUGI 1983 Randomization Tests Robert M. Hamer source 9
SUGI 1983 Stratified Randomization Using SAS Joanne DiPietro,
Sampson Tu		 source
SUGI 1981 PROC RANDOMTEST: A SAS Procedure Performing Randomization Tests Robert M. Hamer source 6
SUGI 1980 A Macro for Multivariate Randomization Analyses of Stratified Sample Data Ingrid A. Amara,
Gary G. Koch		 source 11
SUGI 1979 Randomization Schedules via SAS John L. Stedl source 3

Back to keywords