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baseline characteristics

47 papers and 7 presentations; 52 titles.

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Conference × Year × Title × Authors Extras Pages
PharmaSUG 2026 Dynamic table generation for ongoing studies with unstable or changing cohorts Haoran Li,
Junruo Xia,
Yixuan Zhang		 source 6
PHUSE US 2026 Building High-Quality SDTM from Real-World Data: Lessons and Solutions Peishan(Jenny) Han,
Lixiao Chen		 source
presentation source		 4
PHUSE APAC 2026 Annualized Relapse Rate and Clinical Profiles in South Korean Multiple Sclerosis Patients: A Sample Cohort Study Using Korean National Health Insurance Data Seongjun Byun,
Jisuk Jo		 source
presentation source		 8
PHUSE APAC 2026 Leveraging Large Language Models (LLMs) for Missing Data Imputation and Interpretation of Real-World Evidence Outputs Spencer Langerman,
Rahul Manohar Somavanshi		 source
presentation source		 12
PHUSE APAC 2026 Resistance Data Revolution: Insights, Challenges and FDA Compliance Implementation Sujith Mididoddi source
poster source		 3
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 Multiple Imputations Overview of Missing Data Problem, Solution Methods and SAS Implementation Dmytro Kalinin source
presentation source		 13
PharmaSUG 2025 Practical Process in SAS of Using External Controls Hui Mao,
Na Wang,
Lixin Gao		 source 5
PHUSE US 2025 Streamlining Machine Learning Modelling in Neuroscience Clinical Trials Through Cross-Functional Collaboration Jing Dai source
presentation source		 5
PHUSE US 2024 Future-Proofing Clinical Data Sciences: The Pivotal Role of Therapeutic Area Proficiency Sascha Ahrweiler source
presentation source		 12
PHUSE EU 2023 A Beginner's Guide to Overcome the Attrition Bias in Clinical Research: The Method of Inverse Probability of Attrition Weighting (IPAW) Parichay Srivastava source
presentation source		 9
PHUSE EU 2023 Inference on Weighted Subjects: Comparing Weighted Individual Patient Data with Results from the Literature [poster] Andrea Parodi,
Dimitri Petratchenko		 source 1
PharmaSUG 2023 Automated Mockup Table and Metadata Generator Jeff Cheng,
Shunbing Zhao,
Guowei Wu,
Suhas R. Sanjee		 source 12
PharmaSUG 2023 Macro To Automate Crossover Review In Produced Outputs Igor Goldfarb,
Ella Zelichonok		 source 25
PharmaSUG 2023 Novel Applications of Real World Data in Clinical Trial Operations: Clinical Trial Feasibility Sherrine Eid,
Samiul Haque,
S. Robert Collins		 source 13
PharmaSUG 2023 Statistical Considerations and Methods for Handling Missing Outcome Data in Clinical Trials During the Era of COVID-19 Xi Qian,
Chengfei Lu		 source 11
PHUSE US 2023 Display Layout Specifications to Flexibly Design and Generate Tables [presentation] Songgu Xie,
Michael Pannucci		 source
video		 29
PHUSE EU 2022 Using Individual-level Real-world Data to Inform Disease Area Data Models Employed for Clinical Trial Simulation and Design [presentation] Sheila McCartan source
video		 41
PHUSE EU 2021 CDISC Analysis Results Standard Team: Enhancing Analysis Results and Traceability [presentation] Bess LeRoy,
Jeffrey M. Abolafia		 source
video		 41
PHUSE EU 2021 Preparing PK/PD ADaM Datasets and eCRT Data Package for FDA Submission Junying Zhang source
video
presentation source		 11
PHUSE EU 2021 The Art of Swim Lane Plot Ganesh Lekurwale source
video
presentation source		 7
PharmaSUG 2021 A Programmer's Experience Researching Real-World Evidence (RWE) COVID-19 Data Ginger Barlow source 5
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
SCSUG 2018 'V' for ... Variable Information Functions to the Rescue Richann J. Watson,
Karl Miller		 source 16
PHUSE EU 2018 Propensity Score Matching Stephanie Fechtner source
presentation source		 9
PHUSE US 2018 Convert the Clinical SAS datasets from RTF files [presentation] Murali Neela source 10
SGF 2018 !WRONG PAPER FIEL - WAITING for FIX! Nikki M. Carroll source 9
SGF 2018 Application of Propensity Score Models in Observational Studies Nikki M. Carroll source 9
PHUSE EU 2016 Analysis Datasets for Baseline Characteristics and Exposure Time for Multi-stage Clinical Trials Kimberly Umans,
Katherine Riester		 source
presentation source		 5
PHUSE EU 2016 Considerations in the Use of Propensity Scoresin Observational Studies Lawrence Rasouliyan,
Estel Plana		 source
presentation source		 17
PHUSE EU 2016 Integrating the New EMA Requirements on Public Disclosure in the Study Conduct [presentation] Jean-Marc Ferran source 20
PHUSE EU 2016 The TFL Workbench: Tools to Standardise and Accelerate Table, Figure and Listing (TFL) Programming Iain Humphreys,
Hansjörg Frenzel		 source
presentation source		 10
SGF 2016 Integrating SAS and R to Perform Optimal Propensity Score Matching Lucy D'Agostino McGowan,
Robert Greevy		 source 11
MWSUG 2015 'V' for ... Variable Information Functions to the Rescue Richann J. Watson,
Karl Miller		 16
PHUSE EU 2015 Bias Reduction in Observational Cohort Studies - Obtaining Matched Cohorts Using Propensity Scores Shelley Fordred,
Victor Kiri		 source
presentation source		 8
SESUG 2015 A SAS Algorithm for Imputing Discrete Missing Outcomes Based on Minimum Distance Minimum Distance source 7
PharmaSUG 2015 Challenges in Developing ADSL with Baseline Data Hongyu Liu,
Hang Pang		 source 6
PharmaSUG 2015 It Depends On Your Analysis Need Sandra Minjoe source 10
MWSUG 2014 'V' for ... Variable Information Functions to the Rescue Richann J. Watson,
Karl Miller		 16
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
WUSS 2014 A SAS Macro to Produce Summary Statistics Tables Allowing Additional Stratification Amanda Liu,
David E. Tappe		 source 5
PharmaSUG 2014 'V' for ... Variable Information Functions to the Rescue Richann J. Watson,
Karl Miller		 source 15
WUSS 2013 Customize Comparison Tables for Clinical Studies Polina Kukhareva,
Katherine J. Roggenkamp		 source 12
SCSUG 2012 POSTER: ADaM Implications of the CDER Data Standards Common Issues Document Sandra Minjoe source 6
PHUSE EU 2012 A Simple Interface for Meta-Data [presentation] Magnus Mengelbier source 18
PHUSE EU 2012 Bootstrap simulations to estimate Overall Survival based on the distribution of a historical control Antonio Nieto,
Javier Gómez		 source
presentation source		 7
PharmaSUG 2012 ADaM Implications from the "CDER Data Standards Common Issues" and SDTM Amendment 1 Documents Sandra Minjoe source 6
PharmaSUG 2012 CDISC ADaM Application: Does All One-Record-per-Subject Data Belong in ADSL? Sandra Minjoe source 10
SGF 2012 Standardized Difference: An Index to Measure the Effect Size between Two Groups Dongsheng Yang,
Jarrod E. Dalton		 source 6
MWSUG 2011 Increased Life Expectancy from Positive Perceptions of Retirement Reuben Ng,
Deepak C. Lakra,
Becca R. Levy		 10
NESUG 2010 An Introduction to Implementing Propensity Score Matching with SAS Kathy Hardis Fraeman 12
PHUSE EU 2009 ClinicalTrials.gov - a programmers perspective Ralf Minkenberg source
presentation source		 6
PharmaSUG 2009 A SAS Macro Application for Generating Patient-Level Reports in Clinical Studies Yao Huang 4

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