| PHUSE US |
2026 |
Practical Survival Analysis Techniques for Clinical Trials Using SAS |
Xue Ding,
Tianchen Xu |
source
presentation source |
10 |
| PHUSE EU |
2025 |
How to Identify and Operationalize GenAI Use Cases Which Really Help You |
Christoph Bergen,
Luis Wirth |
source
presentation source |
8 |
| SESUG |
2025 |
Time Series Analysis and Forecasting of U.S. Teenage Female Unemployment |
Sekyiwaah Nuamah |
|
21 |
| PHUSE US |
2025 |
Streamlining Machine Learning Modelling in Neuroscience Clinical Trials Through Cross-Functional Collaboration |
Jing Dai |
source
presentation source |
5 |
| PHUSE EU |
2024 |
Streamlining Complex Analysis: An SAS Macro for Marginal Structural Modelling |
Amine Khemiri |
source
presentation source |
6 |
| PharmaSUG CN |
2024 |
Application of Natural Language Processing with R in clinical trials |
Feifeng Zhou,
Zhenchao Chen |
source |
11 |
| PHUSE US |
2024 |
Applying Machine Learning for Predicting Potential Adverse Events from Real-World Data |
Samiul Haque |
source
presentation source |
6 |
| WUSS |
2023 |
Leveraging Teradata ClearScape Analytics to Improve ROI [presentation] |
Gregory Goralski,
Paul Segal |
source |
40 |
| SESUG |
2022 |
A SAS Macro That Automates Model Fitting of Group-Based Trajectory Modeling Using Proc TRAJ |
Donald Warden,
Yu Jiang |
source |
9 |
| SESUG |
2022 |
Don't be so One-Dimensional: How to Engineer Multi-Dimensional, High Cardinality Categorical Inputs for Machine Learning |
Aleksandar Nikolic |
source |
25 |
| SESUG |
2022 |
Embracing Cross-Loading to Improve Latent Variable Models Fit: A comparison of available options in SAS, Mplus, and R |
Kevin B. Gittner,
Niloofar Ramezani,
Katherine Mobley |
source |
26 |
| PHUSE US |
2022 |
Using the Nest-map-unnest Workflow in R for Sub-group Analysis in Clinical Trial Reporting [presentation] |
Mia Qi,
Sheng-Wei Wang |
source
video |
12 |
| PHUSE EU |
2021 |
Deep Active Learning for Clinical Entity Recognition [presentation] |
Nadia Abraham |
source
video |
33 |
| SESUG |
2021 |
Screening, Binning, Transforming Predictors for a Generalized Logit Model |
Bruce S. Lund |
source |
17 |
| PHUSE US |
2021 |
Using Computer Vision to Identify and Extract Tables from Various File Formats [presentation] |
Kanishk Mair,
Shashank Pagidimarri |
source
video |
22 |
| SGF |
2021 |
Automated Vehicle Odometer Reading Using SAS for AI and Computer Vision |
Tyler Rosacker,
Kedar Prabhudesai,
Hardi Desai,
Neela Vengateshwaran,
Varunraj Valsaraj |
source
pdf |
17 |
| SGF |
2021 |
Weight of Evidence, Dummy Variables and Degrees of Freedom |
Bruce S. Lund |
source
sas
presentation
pdf |
15 |
| SGF |
2020 |
Deploying Computer Vision by Combining Deep Learning Action Sets with Open-Source Technology |
Jonny McElhinney,
Duncan Bain,
Haidar Altaie |
source |
20 |
| SGF |
2020 |
Explanatory Machine Learning Model Plots for Epidemiological and Real-World Evidence Studies |
David Olaleye |
source |
22 |
| SGF |
2020 |
How to Build a Text Analytics Model in SAS Viya with Python |
Nate Gilmore |
source |
13 |
| SGF |
2020 |
Model Governance for AI: Rethinking Our Approach to Validating and Using Black Box Models |
Naeem Siddiqi |
source |
5 |
| SGF |
2020 |
Neural-Network Based Forecasting Strategies in SAS Viya |
Steven C. Mills |
source |
14 |
| SGF |
2020 |
Noninvasive Beehive Monitoring through Acoustic Data Using SAS Event Stream Processing and SAS Viya |
Yuwei Liao |
source |
24 |
| SGF |
2020 |
Turning the Crank. A Simulation of Optimizing Model Retraining |
David R. Duling |
source |
17 |
| PharmaSUG |
2020 |
How to let Machine Learn Clinical Data Review as it can Support Reshaping the Future of Clinical Data Cleaning Process |
Mirai Kikawa,
Yuichi Nakajima |
source |
9 |
| PHUSE EU |
2019 |
Preparing Big Data for Analysis |
Thomas Ellebæk |
source
poster source |
4 |
| SGF |
2019 |
How to Use Deep Learning with Your Internet of Things (IoT) Digital Twin |
Bradley W. Klenz |
source |
9 |
| SGF |
2019 |
Screening, Transforming, and Fitting Predictors for the Cumulative Logit Model |
Bruce S. Lund |
source |
21 |
| SGF |
2019 |
The Aftermath What Happens after you Deploy Your Models and Decisions |
David R. Duling |
source |
18 |
| SCSUG |
2018 |
Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model |
Mei Najim |
source |
17 |
| SESUG |
2018 |
Predictive Modeling for Healthcare Professionals: The use of time-series analysis for health-related data and the application of ARIMA modeling techniques in SAS for Public Health Practice |
Camillia R. Comeaux |
source |
6 |
| MWSUG |
2018 |
Screening, Transforming, and Fitting Predictors for Cumulative Logit Model |
Bruce S. Lund |
|
22 |
| SGF |
2018 |
Claim Analytics: A Litigation Prediction Case Study |
Mei Najim |
source |
18 |
| SGF |
2018 |
Credit Score: A Comparison of Gradient Boosting with Logistic Regression in Practical Cases |
Paulo Di Cellio Dias,
Marc Witarsa |
source |
25 |
| SGF |
2018 |
Image Processing: Seeing the World through the Eyes of SAS Viya |
Ivan Gomez |
source |
11 |
| SGF |
2018 |
Improved Campaigning at Africa's Largest Pay TV Service Provider with SAS Marketing Optimization |
Jean De Villiers |
source |
16 |
| SGF |
2018 |
Robust Tuning for Machine Learning |
Alex Glushkovsky |
source |
22 |
| SGF |
2018 |
Using SAS for Multiple Imputation and Analysis of Longitudinal Data |
Patricia A. Berglund |
source |
24 |
| SGF |
2018 |
Using SAS to Fit AmeriFlux Data to Ecosystem Seasonality Models |
Tracy Song-Brink |
source |
6 |
| MWSUG |
2017 |
Claim Analytics |
Mei Najim |
|
18 |
| MWSUG |
2017 |
Untangle Customer's Incrementality using Uplift Modeling with a Case Study on Direct Marketing Campaign |
Hairong Gu,
Yi Cao,
Chao Xu |
|
8 |
| SGF |
2017 |
Analytics Using SAS Customer Intelligence: Multivariate Testing Processes for Digital Marketing Campaigns |
Thomas Lehman |
source |
9 |
| SGF |
2017 |
Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data |
Josephine Akosa |
source |
12 |
| SGF |
2017 |
Property and Casualty Insurance Predictive Analytics in SAS |
Mei Najim |
source |
17 |
| SESUG |
2016 |
SAS Macro for Automated Model Selection Involving PROC GLIMMIX and PROC MIXED |
Fan Pan,
Jin Liu |
source |
9 |
| PHUSE EU |
2016 |
Proposal of a General Macro Framework for Propensity-score Matching [presentation] |
Holger Langkabel |
source |
23 |
| MWSUG |
2016 |
Property and Casualty Insurance Predictive Analytics in SAS |
Mei Najim |
|
17 |
| SGF |
2016 |
A Property and Casualty Insurance Predictive Modeling Process in SAS |
Mei Najim |
source |
14 |
| SGF |
2016 |
Can You Stop the Guesswork in Your Marketing Budget Allocation? Marketing Mixed Modeling Using SAS Can Help! |
Delali Agbenyegah |
source |
7 |
| SCSUG |
2015 |
Using SAS Software to Determine the Mixed Rotterdam Demand Model Parameters and Test Statistical References |
Maryam Tabarestani,
Hector Zapata |
|
23 |
| MWSUG |
2015 |
A Property & Casualty Insurance Predictive Modeling Process in SAS |
Mei Najim |
|
14 |
| MWSUG |
2015 |
Can You Stop the Guesswork in Your Marketing Budget Allocation? Marketing Mixed Modeling Using SAS Can Help! |
Delali Agbenyegah |
|
7 |
| PHUSE EU |
2014 |
Small Sample Bayesian Factor Analysis |
Dirk Heerweghm |
source
presentation source |
18 |
| SGF |
2014 |
Modeling Fractional Outcomes with SAS |
Wensui Liu,
Jason Xin |
source |
17 |
| SESUG |
2012 |
Random Effects Simulation for Sample Size Calculations Using SAS |
Random Effects Simulation |
source |
11 |
| SGF |
2012 |
Its About Time: Discrete Time Survival Analysis using SAS Enterprise Miner |
Sascha Schubert,
Susan Haller,
Taiyeong Lee |
source |
15 |
| SGF |
2011 |
Making Use of Incomplete Observations in the Analysis of Structural Equation Models: The CALIS Procedures Full Information Maximum Likelihood Method in SAS/STAT 9.3 |
Yiu-Fai Yung,
Wei Zhang |
source |
20 |
| PHUSE EU |
2010 |
Assesment of Cox Proportional Hazard Model Adequacy Using PROC PHREG and PROC GPLOT |
Jadwiga Borucka |
source
presentation source |
13 |
| SGF |
2010 |
The Power of the Group Processing Facility in SAS Enterprise Miner |
Sascha Schubert |
source |
13 |
| SESUG |
2008 |
A macro for nearest neighbor imputation |
Lung-Chang Chien |
source |
6 |
| SESUG |
2008 |
Tailoring the use of SAS Enterprise Miner |
Sascha Schubert |
source |
14 |
| PHUSE EU |
2008 |
Statistically guided review of safety data in clinical trials |
Michael O'Connell,
Harry Southworth |
source |
12 |
| SGF |
2008 |
Tailoring the use of SAS Enterprise Miner |
Sascha Schubert |
source |
14 |
| NESUG |
2007 |
Customizing Output for Regression Analyses Using ODS and the Data Step |
Zhenyi Xue |
|
11 |
| MWSUG |
2007 |
Best Practices for Managing Predictive Models in a Production Environment |
Robert Chu |
|
10 |
| SGF |
2007 |
Best Practices for Managing Predictive Models in a Production Environment |
Robert Duling,
David M. Thompson,
Wayne Chu |
source |
10 |
| SGF |
2007 |
SAS Macros for Automated Model Selection When the Response Variable Is Nominal-Needs and Solutions Involving PROC CATMOD |
Bonnie E. Williams,
Todd Kegan |
source |
12 |
| PharmaSUG |
2006 |
Generating Half-normal Plot for Zero-inflated Binomial Regression |
Zhao Yang,
Xuezheng Sun |
|
12 |
| NESUG |
2002 |
Forecasting with the Time Series Forecasting System in SAS/ETS |
Charles B. Hallahan |
|
6 |
| SEUGI |
2002 |
SAS Enterpise Miner: What does the future hold? [presentation] |
David R. Duling,
Sascha Schubert |
source |
45 |
| SUGI |
2002 |
Robust Regression and Outlier Detection with the ROBUSTREG Procedure |
Colin Chen |
source |
13 |
| NESUG |
2001 |
The Time Series Forecasting System |
Charles B. Hallahan |
|
6 |
| SUGI |
2000 |
The Time Series Forecasting System |
Charles B. Hallahan |
source |
9 |
| MWSUG |
1999 |
Longitudinal Data Analysis Repeated-Measure ANOVA and Mixed Models |
Nancy E. Briggs,
Ching-Fan Sheu |
|
6 |
| MWSUG |
1999 |
Using PROC MIXED in Hierarchical Linear Models: Examples from two- and three-level school-effect analysis, and meta-analysis research |
Sawako Suzuki,
Ching-Fan Sheu |
|
8 |
| SUGI |
1999 |
Fitting a Multisource Regression Model with Random Slopes, A Fisheries Application of SAS PROC MIXED |
Robert G. Downer,
Mark C. Benfield |
source |
5 |
| SESUG |
1998 |
The Effect of Missing Data on Repeated Measures Models |
Maribeth H. Johnson |
|
5 |
| SUGI |
1997 |
Estimating Nonlinear Systems Using Customized SAS Macros: A Study on Demand for Medical Products and Services in the U.S. |
Jun Zuo,
Guijing Wang |
source |
6 |
| SUGI |
1997 |
Quantitation of Interactive Effects between Anticancer Agents Using SAS Macros and SAS/GRAPH |
Yalan Wang,
Jun Zuo,
M. Guill. Wientjes |
source |
6 |
| SUGI |
1993 |
Using SAS/IML Software to Generate Odds Ratios and Associated Intervals Based on Logistic Regression Output |
Catherine O. Cowie,
Danita D. Byrd-Hol,
Keith F. Rust |
source |
6 |
| SUGI |
1987 |
The Analysis of Mapped Spatial Point Patterns |
E. Barry Moser |
source |
5 |
| SUGI |
1982 |
A Pharmacokinetic Analysis System (PAS) |
Raymond P. Chen,
Daniel M. Wildman |
source |
|
| SUGI |
1980 |
Fitting Time Series Using SAS |
David M. DeLong |
source |
5 |
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