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1222 papers and 43 presentations; 1153 titles.

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Conference × Year × Title × Authors Extras Pages
WUSS 2026 Demystifying Regression Analysis Techniques with R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 35
WUSS 2026 From Prediction to Explanation: Per-Patient SHAP Attribution for a Random Forest Diabetes Risk Classifier Leon Rod Davoody,
Chary Akmyradov		 source 12
WUSS 2026 Interactive Modeling with SAS Visual Statistics Danny R. Modlin source 28
PharmaSUG CN 2026 An Integrated, AI-Augmented R Shiny Platform for Oncology Clinical Trial Decision Intelligence: From CDISC Data to Go/No-Go in One Unified Hub Zhiping Yan source 28
PharmaSUG CN 2026 Automated Clinical Text Classification with Robust Machine Learning for Pharmaceutical Data Curation Weineng Zhou source 11
PharmaSUG 2026 Contrast Effects in Curated Observational Data Shankar Srinivasan,
Helen Guo,
Yvonne Buttkewitz		 source 16
PharmaSUG 2026 Machine Learning Models for Predicting Diabetes Using the PIMA Indians Dataset Leon Rod Davoody source 11
PharmaSUG 2026 Regression Analysis Made Easy Using R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 source 32
PHUSE US 2026 Acquiring a Compound with Multiple Clinical Trials? Let's Get Ready for a Type C Meeting! Key Considerations for Preparing Its Regulatory Submission Mathura P. Ramanathan,
Kajal Tahiliani		 source
presentation source		 12
PHUSE US 2026 AI-Driven Quality Risk Management for Contract Manufacturing Oversight: An ML Approach Kiran Kumar Gande source
presentation source		 7
PHUSE US 2026 Demystifying TMLE: A Practical Approach for Clinical Data Scientists Joshua Enxing source
presentation source		 12
PHUSE US 2026 Multiple Sclerosis Trials Unpacked: Standards, Endpoints and Programming Experience Daryna Teteriuk source
presentation source		 12
PHUSE US 2026 Robust Causal Inference in Real-World Evidence Studies with Double Machine Learning Yilun Huang,
Jing Dai		 source
presentation source		 5
PHUSE APAC 2026 From Data to Digital Phenotypes: Modelling High-Dimensional PROs and Actigraphy Data in Decentralized Psychiatric Trials Sakshi Kaushik source
presentation source		 14
PHUSE EU 2025 An Integrated J&J Platform To Enable Sustainable Clinical Trial Design Archana Nayak,
Jason LaRoche		 source
presentation source		 7
PHUSE EU 2025 Evaluating the Use of Real-World Data (RWD) as a Synthetic Control Group: A Statistical Approach Vasiliki Economou,
Dimitrios Kounelis		 source
poster source		 17
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 Large Language Models as Medical Writers: Challenges and Solutions Ioannis Reklos,
Alexandrina Lambova		 source
presentation source		 10
PHUSE EU 2025 Multiple Imputations Overview of Missing Data Problem, Solution Methods and SAS Implementation Dmytro Kalinin source
presentation source		 13
MWSUG 2025 Code Smarter, Not Harder: The 5 C's of ChatGPT for the SASsy Professional Kirk Paul Lafler,
Charu Shankar		 source 49
MWSUG 2025 Feature Selection and Classification Using Fuzzy Logic Ross Bettinger source 39
MWSUG 2025 How to Modify SAS 9 Programs to Run in SAS Viya Danny R. Modlin source 40
MWSUG 2025 Logistic Regression Odyssey on a Small Sample Due to Rare Cancer [presentation] Brandy R. Sinco,
Jessie Dalman,
Tasha Hughes		 source 46
SESUG 2025 Easily Turn Your Automated Explanation into a Predictive Model Danny R. Modlin 21
SESUG 2025 Multiple Logistic Regression Using SAS and SPSS Amita Patil 14
PharmaSUG 2025 Association of Alzheimer-s Disease with Cardiovascular Disease and Depression in Older Adults: Findings from the 2018 Nationwide Inpatient Sample Prayag Shah source 27
PharmaSUG 2025 Code Smarter, Not Harder: The 5 C's of ChatGPT for the SASsy Professional Charu Shankar,
Kirk Paul Lafler		 source 49
PharmaSUG 2025 Methodology for AI-driven Outcome Prediction for Patients with Atrial Fibrillation After Transcatheter Aortic Valve Implantation (TAVI) Felix Just,
Krishna Padmanabhan,
Parth Jinger,
Amanda Borrow,
Rüdiger Smolnik,
Eva-Maria Fronk,
Neelam Yadav,
Sergei Krivtcov,
Stas Zakharkin,
Martin Unverdorben,
Asmir Vodencarevic		 source 6
PharmaSUG 2025 Practical Process in SAS of Using External Controls Hui Mao,
Na Wang,
Lixin Gao		 source 5
PharmaSUG 2025 Super Learner for Predictive Modeling and Causal Analysis Honghe Zhao,
Clay Thompson,
Michael Lamm		 source 9
PHUSE US 2025 Applications of Machine Learning and Artificial Intelligence in Real-World Data in Personalised Medicine for Non-Small Cell Lung Cancer Patients Samiul Haque source
presentation source		 8
PHUSE US 2025 Comparing Confidence Intervals for a Difference in Adjusted Predicted Probabilities Cralen Davis,
Trevano Dean		 source
presentation source		 5
MWSUG 2024 What if Proc Power Can't Help? Calculating Power via Simulation, Example with Count Regression Brandy R. Sinco,
Joceline Vu,
Pasithorn Suwanabol		 source 16
PHUSE EU 2024 Leveraging 'Ensemble' Machine Learning Models to Drive Automation of Specification Generation and Data Mapping in Clinical Trial Data Submission Venu Mallarapu source
presentation source		 10
PHUSE EU 2024 Using RWD to Inform the Next Study: A Case Study [presentation] Jean-Marc Ferran source 22
SESUG 2024 A Comprehensive Analysis of the Relationship Between Depression, Sleep Deprivation, and Self-Esteem Using Advanced Statistical Methods Muslihat Adejoke Gaffari source 43
SESUG 2024 Comparative Analysis of Sentiment Prediction Methods Using Aspects in Consumer Reviews Harish Palakurthi source 8
SESUG 2024 Interactive Modeling with SAS Visual Statistics Danny R. Modlin source 28
SESUG 2024 Investigating the Role of Radiomics in PET Image Analysis for Improved T-Stage Tumor Staging Victor Aderanti source 31
SESUG 2024 Unlocking Insights: Harnessing Supervised Machine Learning for Advanced Data Analysis Vindhya Hegde source 23
WUSS 2024 Airbnb Consumer Behavior Analysis Evelyn Larson,
Anabeli Chandra		 source 14
WUSS 2024 Developing a SAS Macro for ISNI: Assessing Missing Data Sensitivity Beyond MAR Assumptions Bocheng Jing,
L. Grisell Diaz-Ramirez,
W. John Boscardin		 source 16
WUSS 2024 Flight Delay Analysis and Prediction Eric C Braga,
Miguel Ángel Bravo Martínez del Valle,
Riley Rutan,
Fernanda Carrillo		 source 19
WUSS 2024 From Keys to Credit: A Deep Dive into Homeownership's Influence on Loan Quality Yi-Che Chen,
Debanik Chakraborty		 source 15
WUSS 2024 Integration of an R Language Function Within SAS to Perform Conditional Multivariate Saddlepoint Approximation Christopher Johnson source 8
WUSS 2024 Limit of Detection Calculation Methods Comparison for PCR-based Quantitative Studies using the SAS System Jesse Albert Canchola source 13
WUSS 2024 Regression Analysis Made Easy Using SAS Studio Zheyuan (Walter) Yu source 42
PharmaSUG CN 2024 Application of I&I Late Phase in Programming Renxiang Wang source 15
PharmaSUG 2024 Applications of Machine Learning and Artificial Intelligence in Real World Data in Personalized Medicine for Non-Small Cell Lung Cancer Patients Sherrine Eid,
Samiul Haque,
S. Robert Collins		 source 8
PharmaSUG 2024 Leveraging ChatGPT in Statistical Programming in the Pharmaceutical Industry Ian Sturdy source 24
PharmaSUG 2024 MLNR or Machine Learning in R Chuck Kincaid source 23
PharmaSUG 2024 Multiple Logistic Regression Analysis using Backward Selection Process on Objective Response Data with SAS Girish Kankipati,
Jai Deep Mittapalli		 source 9
PHUSE US 2024 Case study of verification and validation methods for algorithmic predictive biomarkers Kartik Shetty source
presentation source		 4
PHUSE US 2024 How No-Code Visualisation is Fast Becoming an Asset for Data Engineering and Data Science Teams Pritesh Desai source
presentation source		 6
PHUSE US 2024 Risk-Based Monitoring Using AI/ML Detection of Systematic Bias in Clinical Data Kostiantyn Drach,
Sergey Glushakov		 source
presentation source		 12
PHUSE US 2024 The Role of AI (ML/NLP/Deep Learning) in Handling Sensitive/Confidential Data Divya Kanniah,
Swetha Balaji		 source
presentation source		 3
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 Office-based or Home-based: Are We Remotely Close to the New Steady State? A Working Session Michael S Rimler source
presentation source		 4
WUSS 2023 Introduction to Machine Learning - Descriptions and Best Practices Jim Box source 13
WUSS 2023 Leveraging Logistic Regression and Bootstrap Sampling to Assess a Decision Threshold in Classification Predictive Analytics Colleen E. McGahan source 15
WUSS 2023 Regression Analysis Made Easy Using SAS Studio Kirk Paul Lafler,
Zheyuan (Walter) Yu,
Nuoer Lu,
Nicklas (Rebel) Yee,
Yanzhang Gavin Chen,
Kai Kang		 source 43
WUSS 2023 Survey Data Analysis in SAS Denis Nyongesa,
Marianne Jurasic,
Gregg Gilbert,
Tamara Lischka		 source 11
WUSS 2023 The Delta Method in Statistical Inference, with Applications in the SAS IML Procedure Carter Sevick,
Camille Moore,
Samantha MaWhinney		 source 14
SESUG 2023 Binning Predictors for Logistic Regression Bruce S. Lund source 25
SESUG 2023 Navigating First-Year STEM Student Persistence: Insights from Student Experience and Expectations Zhong Zheng source 1
SESUG 2023 Performing Higher Education Enrollment Management Predictions Using SAS Phil Moore,
Marla Mamrick,
Sara Reinhardt		 source 13
SESUG 2023 Regression Analysis Made Easy Using SAS Studio Kirk Paul Lafler,
Zheyuan (Walter) Yu,
Nuoer Lu,
Daniel Qian,
Kai Kang,
Yanzhang Gavin Chen,
Nicklas (Rebel) Yee,
Yixuan (Jason) Xiang		 source 43
PharmaSUG 2023 Application of Tipping Point Analysis in Clinical Trials using the Multiple Imputation Procedure in SAS Yunxia Sui,
Xianwei Bu,
Yuanyuan Duan,
Yihan Li,
Xin Wang		 source 12
PharmaSUG 2023 BIMO Package: Challenges and Perspectives while keeping up with the upgrades in the BIMO Technical Conformance Guide and the BDRG Guidelines Mathura P. Ramanathan,
Sowmya Gabbula		 source 18
PharmaSUG 2023 Survival Methods for Crossover in Oncology Trials Brian Mosier source 18
PharmaSUG 2023 What is Machine Learning, anyway? Jim Box source 11
PHUSE US 2023 RBM and Failure Prediction Using Python for Health Monitoring Devices Vidya Muthukumar source
presentation source		 7
SESUG 2022 A Two-Staged Local Regression Based Binning Method for Weight of Evidence Transformation in Credit Scoring Models Hui Wang,
Shirong Huang,
Emma Zhou,
Erin Martin		 source 30
SESUG 2022 Invoking Survey Weights to Calculate Bayes' Factor with PROC MCMC to Generate More Generalizable Inferences Tyler Hicks,
Graham G. Rifenbark		 9
WUSS 2022 Story telling with SAS Visual Statistics Jim Box,
Laura Watson		 source 8
WUSS 2022 Study of cause and effect in medical research via SAS Statistical package Oleg Korovyanko source 11
PharmaSUG 2022 Estimating Differences in Probabilities (Marginal Effects) with Confidence Interval Jun Ke,
Kelly Chao,
Corey Evans		 source 4
PharmaSUG 2022 Generating Real World Evidence On The Likelihood Of Metastatic Cancer In Patients Through Machine Learning In Observational Research: Insights For Prevention Sherrine Eid,
Samiul Haque,
S. Robert Collins		 source 8
PharmaSUG 2022 Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software Matthew T. Slaughter,
Isaiah Lankham		 source 22
PHUSE EU 2021 Clinical Design - Visualisation and Analysis Using the Interactive Application by R Shiny Athira Sudhakar,
Mahesh Divakaran		 source
video		 7
PHUSE EU 2021 Evaluating the Predictive Accuracy of Survival Models with PROC PHREG Lotte Jacobs,
Roxane Debrus		 source
video
presentation source		 7
PHUSE EU 2021 HoW: RWD Applications in Pharma with a Focus on Clinical Trials [presentation] Berber Snoeijer,
Samiul Haque		 source 47
SESUG 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source 21
SESUG 2021 Screening, Binning, Transforming Predictors for a Generalized Logit Model Bruce S. Lund source 17
PHUSE US 2021 A Bayesian Approach to Detection of Treatment-related Effects in Toxicology Studies by Borrowing Information from Historical Control Animals [presentation] Kevin Snyder,
Md Yousuf Ali		 source
video		 15
PHUSE US 2021 Comparative Characteristics of Logistic Regression Implementation in SAS and R in the Example of Cardiovascular Diseases [presentation] Serhii Kashyn source
video		 22
PHUSE US 2021 Exploring Categorical Data Using Unsupervised Graph-based Machine Learning [presentation] Irina Kotenko,
Kostiantyn Drach		 source
video		 34
PHUSE US 2021 Parmsurv: An SAS Macro for Flexible Parametric Survival Analysis with Long-term Predictions Han Fu,
Shahrul Mt-Isa		 source
video
presentation source		 10
PharmaSUG 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source 21
PharmaSUG 2021 Mitigating Multicollinearity: Using Regulation Techniques to Minimize the Effect of Collinearity Deanna Naomi Schreiber-Gregory source 23
SGF 2021 A Framework for Simple and Efficient Bootstrap Validation in SAS, with Examples Isaiah Lankham,
Matthew T. Slaughter		 source 22
SGF 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source
presentation		 20
SGF 2021 Do I Have Enough? - A Macro for Sample Size Determination in Simple and Multiple Logistic Regression Carl P. Wilson source
sas		 10
SGF 2021 Montreal Cognitive Assessment, Mini-Mental State Examination: Impairment & Dementia Cutoff Points Feier Han,
Zhen Wu,
Jing Shang,
Yutong Zhang		 source
presentation		 8
SGF 2021 Pandemic Pandemonium Hannah Flynt,
Maryam Taherirani,
Sean Everett,
Trinh Phan		 source 7
SGF 2021 Prediction of Parkinson's Disease With a Regression Model Using Vertical Ground Reaction Force Data Jin Ge,
Tianchen Xu,
Jia Guo,
Mengyu Zhang		 source
presentation		 7
SGF 2021 Weight of Evidence, Dummy Variables and Degrees of Freedom Bruce S. Lund source
sas
presentation
pdf		 15
PHUSE EU 2020 How to Limit Bias in Observational Studies Analysis - Propensity Score Matching Versus Logistic Regression Roxane Debrus source
video
presentation source		 12
PHUSE EU 2020 Testing, Testing [presentation] Ismini Charalambous source
video		 33
SESUG 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 13
SESUG 2020 Model Comparison of Machine Learning Techniques to detect signs of Penetrating Abdominal and Pelvic Injuries using Base JMP and R Integration Melvin T. Alexander source 25
SGF 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 Analyzing Non-normal Data: Application to Missing Data Problems Niloofar Ramezani source 10
SGF 2020 Analyzing the Factors Impacting Suicidal Behavior in American Youth Ashlesha Sampathi source 7
SGF 2020 Automation in SAS Visual Data Mining and Machine Learning Wendy Czika source 26
SGF 2020 Best Practices for Converting SAS Code to Leverage CAS Steven Sober,
Brian Kinnebrew		 source 11
SGF 2020 Developing Credit Scores with Telco Data Using Machine Learning and Agile Methodology Luciano Diettrich source 14
SGF 2020 Explanatory Machine Learning Model Plots for Epidemiological and Real-World Evidence Studies David Olaleye source 22
SGF 2020 From Santiago to the Spirit Lake Nation: 30 Things I Learned in 30 Years as a Statistical Consultant AnnMaria De Mars source 20
SGF 2020 How to Explain Your Black-Box Models in SAS Viya Funda Güneş source 26
SGF 2020 Model-Based Risk-Adjustment in Clinical Outcome Research Jiming Fang,
Feng Qiu		 source 10
SGF 2020 Modelling Imbalanced Classes Humphrey Brydon source 6
SGF 2020 Multinomial vs. Ordinal. Does model selection make a difference? for SAS Global Forum 2020 Corey Leadbeater source 15
SGF 2020 Obtaining National Readmission Estimates: Examining Readmission Rates in the United States Patrick Karabon source 9
SGF 2020 Propensity Score Matching with Survey Data Bocheng Jing source 14
SGF 2020 Rare Events or Non-Convergence with a Binary Outcome? The Power of Firth Regression in PROC LOGISTIC Patrick Karabon source 9
SGF 2020 Relationship analysis and customer churn forecasting in a financial cooperative institution Carolina Silva,
Brunno Sousa Ramos		 source 7
SGF 2020 Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software Isaiah Lankham,
Matthew T. Slaughter		 source
github		 15
SGF 2020 Simulating Data for Complex Linear Models Phil Gibbs,
Kathleen Kiernan		 source 20
SGF 2020 The Seven Most Popular Machine Learning Algorithms for Online Fraud Detection and Their Use in SAS Patrick Maher source 6
SGF 2020 Turning the Crank. A Simulation of Optimizing Model Retraining David R. Duling source 17
SGF 2020 Use SAS to Model Non-Linearity Xiaoting Wu,
Donald S. Likosky		 source 12
SGF 2020 Using Analytics to Predict Tax Recovery and Prioritize Audits and Investigations in Canada Jason A. Oliver source 19
SGF 2020 Variable Selection Using Random Forests in SAS Denis Nyongesa source 11
PharmaSUG 2020 %MVMODELS: a Macro for Survival and Logistic Analysis Jeffrey P. Meyers source 23
PharmaSUG 2020 A Brief Introduction to Performing Statistical Analysis in SAS, R & Python Erica Goodrich,
Daniel Sturgeon		 source 22
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 Demographic Table and Subgroup Summary Macro %TABLEN Jeffrey P. Meyers source 12
PharmaSUG 2020 Effective Exposure-Response Data Visualization and Report by Combining the Power of R , SAS programming and VBScript Shuozhi Zuo,
Hong Yan		 source 11
PharmaSUG 2020 RStats: A R-Shiny application for statistical analysis Sean Yang,
Hrideep Antony,
Aman Bahl		 source 8
PHUSE US 2020 A Comparison of Machine Learning Algorithms for Binary Classification of Free-text Fields Using SAS, R, and Python Michael S Rimler source
presentation source		 12
PHUSE US 2020 Machine Learning for CDISC Specification Recommendations S. Preston Burns source
presentation source		 7
PHUSE US 2020 Penalized Likelihood Logistic Regression for Sparse Data Using Data Priors Martin Dörr source
presentation source		 5
PHUSE EU 2019 Using R to build artificial neural networks in medical data Thomas Wollseifen source
presentation source		 17
SESUG 2019 Advanced Statistical Modeling within Machine Learning and Big Data Analytics Niloofar Ramezani,
George Mason		 source 16
SESUG 2019 Bootstrapping Regression Models using PROC SURVEYSELECT Bryce Whitehead,
Austin R. Brown		 source 7
SESUG 2019 Don't Let Complex Survey Data Get the Best of You! SAS Survey PROCs for Categorical Data Analysis Charlotte Baker source 14
SESUG 2019 Glass Box Neural Networks Ross Bettinger source 14
SESUG 2019 Introduction to Propensity Score Modeling and Treatment Effect Estimation Jason Brinkley source 13
SESUG 2019 Macros for Neural Network Modeling Ross Bettinger source 23
SESUG 2019 Summarizing some conventional methods to classify a binary target Yida Bao,
Philippe Gaillard		 source 10
SESUG 2019 The Thorn in My Side!! Logistic Regression Continuous Variables that Violate the Assumption of Linearity on the Log-odds (Logit) Scale: How to Identify and What to Do? Janet M. Grubber,
Cynthia J. Coffman		 source 13
SESUG 2019 Validating Classification and Recidivism in Corrections Through Outcome Analytics Alan R. Mann source 34
SCSUG 2019 Pattern Analysis and Predictive Policing Using Socio-Economic Factors Trishla Mishra source 12
MWSUG 2019 Classifying Risk in Life Insurance using Predictive Analytics Sai Gopi Krishna Govindarajula 6
MWSUG 2019 Comparison of three methods for transforming predictor variables to improve model fit using SAS Douglas Thompson 13
MWSUG 2019 Do undergraduates need prerequisites for common courses? Lin Qi,
Archana Chinnaswamy,
Miriam McGaugh		 6
MWSUG 2019 Evaluate your SCORE: Logistic regression prediction comparison using the SCORE statement Robert G. Downer 6
MWSUG 2019 Logistic Regression, Basics and Beyond Bruce S. Lund 35
MWSUG 2019 Simulating Skewed Multivariate Distributions Using SAS: Cases of Lomax, Mardia's Pareto (Type I), Logistic, Burr and F Distributions Zhixin Lun,
Ravindra Khattree		 11
MWSUG 2019 Using ODS Trace (DOM), Procedural Output and ODS Output Objects to Create the Output of Your Dreams Louise S. Hadden 26
WUSS 2019 Automating a Summary Report of PSM Model and Match Results Desiree Hall source 9
WUSS 2019 Implementing the Hamiltonian Monte Carlo Sampling algorithm in PROC MCMC Elsa Vazquez Arreola source 10
WUSS 2019 Ordinal vs. Multinomial: Does Model Selection Make a Difference? Corey Leadbeater source 14
WUSS 2019 Statistical Power Calculations Using SAS Software Russell M. Lavery source 18
WUSS 2019 Variable Selection Using Random Forests in SAS Denis Nyongesa source 11
PharmaSUG 2019 An Innovative Efficacy Table Programming to Automate Its Figure Generation to Ensure Both High Quality and Efficiency Xiangchen (Bob) Cui,
Letan (Cleo) Lin		 source 23
PharmaSUG 2019 Bayesian Methods for Treatment Design in Rare Diseases Xuan Sun,
Ruohan Wang		 source 5
PharmaSUG 2019 Experiences in Building CDISC Compliant ADaM Dataset to Support Multiple Imputation Analysis for Clinical Trials Xiangchen (Bob) Cui source 20
PharmaSUG 2019 Hands-on Training for Machine Learning Programming Kevin Lee source 12
PharmaSUG 2019 Logistic and Linear Regression Assumptions: Violation Recognition and Control Deanna Naomi Schreiber-Gregory,
Karlen Bader		 source 21
PharmaSUG 2019 Performing Analytics on Free-Text Data Fields: A Programer-s Wurst Nitemare Michael S Rimler,
Matt Pitlyk		 source 18
PharmaSUG 2019 Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory,
Karlen Bader		 source 23
PharmaSUG 2019 Using Real-World Evidence to Affect the Opioid Crisis Sherrine Eid,
Andrea Coombs		 source 10
PharmaSUG 2019 With a Trace: Making Procedural Output and ODS Output Objects Work For You Louise S. Hadden source 26
SGF 2019 A Comparison of Algorithmic and Epidemiologic Approaches for Modeling Predictors of Type II Diabetes Audrey Whittle source 8
SGF 2019 Are You a Lifelong Learner? Validate Your Skills and Stand Out with SAS Certifications! Andrew Howell source 12
SGF 2019 Developing a Credit Risk Model Using SAS Amos Odeleye source 16
SGF 2019 Don't Let Complex Survey Data Get the Best of You! SAS Survey PROCs for Categorical Data Analysis Charlotte Baker source 16
SGF 2019 Factors Affecting Mental Health in Employees and Their Relation to Suicide Rates at a Workplace Shambhavi Pandey source 11
SGF 2019 Factors Influencing Academic Performance Onkar Mayekar,
Miriam McGaugh		 source 9
SGF 2019 Improve Your AML Monitoring While Reducing Costs with SAS Viya Calvin Crase,
Chris St. Jeor		 source 11
SGF 2019 Integrating Case Studies in a Health Analytics Curriculum Besa Smith,
Tyler C. Smith		 source 21
SGF 2019 Joint Analysis of Failure Times and Time-Varying Covariates Joseph C. Gardiner source 22
SGF 2019 Machine Learning and Predictive Analytics in SAS Enterprise Miner and SAS/STAT Software David Richard Cutler source 21
SGF 2019 Measuring Model Stability Daymond Ling source 15
SGF 2019 Predicting Inside the Dead Zone of Complete Separation in Logistic Regression Robert Derr source 15
SGF 2019 Predicting Patients at High Risk of Hospital-Acquired Conditions Using the LOGISTIC Procedure Jennifer Hargrove,
Pamela Hipp,
Glenn Sidle		 source 12
SGF 2019 Predicting the success of a startup company Vrushank Shah,
Miriam McGaugh		 source 11
SGF 2019 Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory source 23
SGF 2019 SAS Enterprise Miner and SAS Visual Data Mining and Machine Learning Hand Shake Jagruti Kanjia,
Dominique Latour,
Holly Sweeney		 source 35
SGF 2019 SCORE! Techniques for Scoring Predictive Regression Models Using SAS/STAT Software Phil Gibbs,
Randy Tobias		 source 18
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
SGF 2019 The Anatomy of an Artificial Intelligence Solution for Automation of Underwriting Decision-Making Tanya Kolosova source 17
SGF 2019 The Relationship Between Social Determinants of Health and Healthcare Utilization Ryan C. Butterfield,
Melissa Gottschalk,
Laura Soloway,
Paul A. LaBrec		 source 16
SGF 2019 Transforming Ordinal Predictors to Numeric For Binary Logistic Models Bruce S. Lund source 10
SGF 2019 Using Cost-Sensitive Predictive Modeling in Digital Marketing Romana Sipoldova source 7
SGF 2019 Using SAS Procedures to Fit Rasch Models Tianshu Pan,
Yumin Chen		 source 10
SGF 2019 Using SAS to Validate Prediction Models Xiaoting Wu,
Chang He,
Donald S. Likosky		 source 9
SGF 2019 Working with ODS Trace Statement and ODS Output Objects Louise S. Hadden source 26
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 Quantifying Dynamic Risk with Predictive Analytics Susan Olson,
Connor English		 source
presentation source		 7
PHUSE US 2019 Statistical Models of Risk Flux Reveal Dynamics of RBM-managed Clinical Trials Michael Farnum source
presentation source		 12
SCSUG 2018 Backward Elimination in Firth's penalized partial likelihood Approach: Firth's Logistic and Cox Models Chao Zhang source 7
SCSUG 2018 Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model Mei Najim source 17
PHUSE EU 2018 Propensity Score Matching Stephanie Fechtner source
presentation source		 9
PHUSE EU 2018 Serious Adverse Events Identification: A Machine Learning Approach [presentation] Jingshu Liu,
Laura Katz		 source 37
SESUG 2018 Logistic and Linear Regression Assumptions: Violation Recognition and Control Deanna Naomi Schreiber-Gregory,
Henry M. Jackson		 source 22
SESUG 2018 Predicting the Risk of Attrition for Undergraduate Students using SAS Enterprise Miner Sivateja Reddy Kandula source 12
SESUG 2018 Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory,
Henry M. Jackson		 source 23
MWSUG 2018 Confounded? This example shows how to use SAS chi-square tests, correlations and logistic regression to unconfound a result. Michael C. Grierson 13
MWSUG 2018 Logistic and Linear Regression Assumptions: Violation Recognition and Control Deanna Naomi Schreiber-Gregory,
Karlen Bader		 22
MWSUG 2018 Regularization Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory,
Karlen Bader		 23
MWSUG 2018 Screening, Transforming, and Fitting Predictors for Cumulative Logit Model Bruce S. Lund 22
MWSUG 2018 Using your FREQ effectively: Displays to Decipher Proportional Odds in Ordinal Regression Robert G. Downer 11
WUSS 2018 An Efficient Way to Output Univariate Analysis Results with Multiple Predictors Bocheng Jing,
Kathy Fung,
W. John Boscardin		 source 7
WUSS 2018 Logistic and Linear Regression Assumptions: Violation Recognition and Control Deanna Naomi Schreiber-Gregory source 21
WUSS 2018 Machine Learning - Why we should know it and how it works Kevin Lee source 8
WUSS 2018 Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory source 23
WUSS 2018 The Anatomy of Artificial Intelligence Solution for Automation of Underwriting Decision-Making Process. Samuel Berestizhevsky,
Tanya Kolosova		 source 16
PharmaSUG 2018 Build Models without Code with the new SAS Visual Interface Jim Box source 12
PharmaSUG 2018 Generating Receiver Operating Characteristic (ROC) curve using SAS Macros Jiaying Guo,
Isabella Wang		 source 8
PharmaSUG 2018 Machine Learning - Why we should know and How it works Kevin Lee source 8
PharmaSUG 2018 Summary Table for Displaying Results of a Logistic Regression Analysis Lori S. Parsons source 9
SGF 2018 !WRONG PAPER FIEL - WAITING for FIX! Nikki M. Carroll source 9
SGF 2018 A Cautionary Tale of Using Machine Learning within Credit Risk in the Utilities Industry Spiros Potamitis,
Paul Malley		 source 8
SGF 2018 A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius Kathy Hardis Fraeman source 7
SGF 2018 A Study of Modeling Approaches for Predicting Dropout in a Business College Helmut Schneider,
Xuan Wang		 source 8
SGF 2018 Application of Propensity Score Models in Observational Studies Nikki M. Carroll source 9
SGF 2018 Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification Jessica Rudd source 10
SGF 2018 Claim Analytics: A Litigation Prediction Case Study Mei Najim source 18
SGF 2018 Claim Risk Scoring Using Survival Analysis Framework and Machine Learning with Random Forest Yuriy Chechulin source 15
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 Evaluating the Accuracy of Clinical Prediction Models for Binary and Survival Outcomes Joseph C. Gardiner source 21
SGF 2018 Evaluation of Different Approaches to Reject Inference: a case study in Credit Risk Sergiu Luca source 9
SGF 2018 Examining the Drivers of Hospital Re-admission of Type-2 Diabetic patients Shashank Reddy Gudipati source 9
SGF 2018 From Academy to Industry: An Experimental Student Journey [presentation] Francesco Costigliola source 28
SGF 2018 Getting to Know the No-Show: Predictive Modeling of Missing a Medical Appointment Joe Lorenz source 10
SGF 2018 Improved Campaigning at Africa's Largest Pay TV Service Provider with SAS Marketing Optimization Jean De Villiers source 16
SGF 2018 Improving Game Strategies with Data Analytics: The Case of Euroleague Professional Basketball [presentation] Raha Akhavan-Tabatabaei,
Cem Yigman		 source 35
SGF 2018 Insights into Using the GLIMMIX Procedure to Model Categorical Outcomes with Random Effects Kathleen Kiernan source 23
SGF 2018 Model-Based Fiber Network Expansion Using SAS Enterprise Miner and SAS Visual Analytics Nishant Sharma source 5
SGF 2018 Power Analysis for Generalized Linear Models Using the New CUSTOM Statement in PROC POWER John M. Castelloe source 17
SGF 2018 Prediction and Interpretation for Machine Learning Regression Methods David Richard Cutler source 16
SGF 2018 Regression Model Building for Large, Complex Data with SAS Viya Procedures Weijie Cai,
Robert N. Rodriguez		 source 22
SGF 2018 Risk Pathways: Using Machine Learning Techniques of SAS Viya to Understand Customer Risk Drivers Leigh Ann Herhold source 14
SGF 2018 Robust Principal Component Analysis: Two Analyses in One Anthony Salis,
Zohreh Asgharzadeh,
Kyungduck Cha		 source 11
SGF 2018 Robust Tuning for Machine Learning Alex Glushkovsky source 22
SGF 2018 Role of Preventive Care and Lifestyle Changes in Chronic Diseases Asish Satpathy,
Satyajit Behari		 source 13
SGF 2018 The Function Selection Procedure Bruce S. Lund source 11
SGF 2018 TV versus Internet: How Do Americans Consume News? Ethan Stager source 5
SGF 2018 Using Predictive Analytics to Improve a Student Advising System Thanuja Sakruti,
Youyou Zheng		 source 13
SESUG 2017 A macro of building predictive model in PROC LOGISTIC with AIC-optimal variable selection embedded in cross-validation Hongmei Yang source 7
SESUG 2017 Applying Logistics Regression to Forecast Annual Organizational Retirements Alan Dunham source 12
SESUG 2017 Backward Variable Selection for Logistic Regression Based on Percentage Change in Odds Ratio Evan Kwiatkowski source 6
SESUG 2017 Binning of Predictors for the Cumulative Logit Model Bruce S. Lund source 20
SESUG 2017 Churn the Data Around - A Machine Learning Approach to Understanding Why Customers Leave Sean Ankenbruck source 9
SESUG 2017 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 25
SESUG 2017 Statistical Analyses of Public Health Surveys Using SAS Survey Package Trung Ha,
Julia Soulakova		 source 6
SESUG 2017 Using SAS to Employ Propensity Score Matching in an Institutional Research Office to Create Matched Groups for Outcomes Analyses Bobbie E. Frye,
James E. Bartlett		 source 19
SCSUG 2017 Use Machine Learning to Discover Your Rules David R. Duling source 10
PHUSE EU 2017 The Model Statistician Insights into Model Selection Techniques in Clinical Trials Peter Williams source
presentation source		 6
MWSUG 2017 AIR Binder 2.0: A Dynamic Visualization, Data Analysis and Reporting SAS Application for Preclinical and Clinical ADME Assays, Pharmacokinetics, Metabolite Profiling and Identification Hao Sun,
Kristen Cardinal,
Richard Voorman		 26
MWSUG 2017 Claim Analytics Mei Najim 18
MWSUG 2017 Correcting for Selection Bias in a Clinical Trial Shana Kelly 8
MWSUG 2017 Exploring the Relationship Between Substance Abuse and Dependence Disorders and Discharge Status: Results and Implications Deanna Naomi Schreiber-Gregory 25
MWSUG 2017 How Can an NBA Player Be Clutch?: A Logistic Regression Analysis Logan Edmonds 18
MWSUG 2017 Logistic Model Selection with SAS PROC's LOGISTIC, HPLOGISTIC, HPGENSELECT Bruce S. Lund 18
MWSUG 2017 Multiple Imputation of Family Income Data in the 2015 Behavioral Risk Factor Surveillance System Jia Li,
Aaron Sussell		 4
MWSUG 2017 Untangle Customer's Incrementality using Uplift Modeling with a Case Study on Direct Marketing Campaign Hairong Gu,
Yi Cao,
Chao Xu		 8
MWSUG 2017 Using SAS to Compare Two Estimation Methods on the Same Outcome: Example from First Trimester Pregnancy Weights Brandy R. Sinco,
Edith Kieffer,
Kathleen Welch,
Diana Welmerink Bolton		 15
WUSS 2017 An Overview of SAS Visual Data Mining and Machine Learning on SAS Viya Radhikha Myneni source 23
WUSS 2017 Analysis of Complex Survey Data with SAS Christine Wells source 6
WUSS 2017 Analyzing Multiple Membership Hierarchical Data Using PROC GLIMMIX Maria Elsa Vazquez Arreola,
Jeffrey Wilson		 source 7
WUSS 2017 Integrating Health Data Sources to Identify, Stratify, and Predict High Utilizers of Public Systems Benjamin Knisley,
Joshua Morgan,
Linn Carothers,
Keith Haigh		 source 15
WUSS 2017 Is My Model the Best? Methods for Exploring Model Fit Deanna Naomi Schreiber-Gregory source 7
WUSS 2017 NFL Data Analytics for a New Era Mitchell Collins source 14
WUSS 2017 Step Up Your Statistical Practice with Today's SAS/STAT Software Robert N. Rodriguez source 19
PharmaSUG 2017 AIR Binder: An Automatic Reporting and Data Analysis SAS Application for Cytochrome P450 Inhibition Assay to Investigate DDI Hao Sun,
Kristen Cardinal,
Carole Kirby,
Richard Voorman		 source 8
PharmaSUG 2017 Multinomial Logistic Regression Models With Sas Proc Surveylogistic Marina Komaroff source 7
PharmaSUG 2017 The %NEWSURV Family of Macros: An Update on the Survival Plotting Macro %NEWSURV and an Introduction to Expansion Macros Jeffrey P. Meyers source 21
SGF 2017 A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius Kathy Hardis Fraeman source 13
SGF 2017 A Hadoop Journey along the SAS Road Sumit Sukhwani,
Krutharth Peravalli,
Amit Gautam		 source 8
SGF 2017 A Moment-Matching Approach for Generating Synthetic Data in SAS Brittany Bogle,
Jared Erickson		 source 17
SGF 2017 A Practical Guide to Getting Started with Propensity Scores Thomas Gant source 10
SGF 2017 A SAS Macro for Covariate Specification in Linear, Logistic, and Survival Regression Sai Liu source
zip		 19
SGF 2017 An Investigation into Big Data Analytics Applied to Insurance Rebecca Peters,
Penny Holborn		 source 18
SGF 2017 An Overview of SAS Visual Data Mining and Machine Learning on SAS Viya Jonathan Wexler,
Susan Haller		 source 23
SGF 2017 Analyzing Correlated Data in SAS Niloofar Ramezani source 13
SGF 2017 Analyzing Residuals in a PROC SURVEYLOGISTIC Model Bogdan Gadidov source 8
SGF 2017 Construction of a Disease Network and a Prediction Model for Dementia Jinwoo Cho source 14
SGF 2017 Dataninjas: Modeling Life Insurance Risk David Allen,
Seung Lee		 source 7
SGF 2017 Evaluating Predictive Accuracy of Survival Models with PROC PHREG Changbin Guo,
Ying C. So,
Woosung Jang		 source 16
SGF 2017 Examining Higher Education Performance Metrics with SAS Enterprise Miner and SAS Visual Analytics Taylor Blaetz,
Tuesdi Helbig,
Gina Huff,
Matt T. Bogard		 source 15
SGF 2017 Fitting a Cumulative Logistic Regression Shana Kelly source 5
SGF 2017 Fitting Complex Statistical Models with NLMIXED and MCMC Procedures Robin High source 14
SGF 2017 Flow Riders Driving Below Traffic Flow: A Risk Analysis Zhongjie Cai,
Dixin Shen,
Ken Chau		 source 12
SGF 2017 GMM Logistic Regression with Time-Dependent Covariates and Feedback Processes in SAS Jeffrey Wilson source 7
SGF 2017 I Am Multilingual: A Comparison of the Python, Java, Lua, and REST Interfaces to SAS Viya Xiangxiang Meng,
Kevin D. Smith		 source 16
SGF 2017 Interactive Modeling in SAS Visual Analytics Donald R. Chapman,
Jonathan Wexler		 source 16
SGF 2017 Modeling the Merchandise Return Behavior of Anonymous and Non-Anonymous Online Apparel Retail Shoppers Sunny Lam source 20
SGF 2017 Optimizing Delivery Routes with SAS Software Ben Murphy,
Bruce Bedford		 source 13
SGF 2017 Outline Outliers: Adding a Business Sense Alex Glushkovsky source 17
SGF 2017 Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data Josephine Akosa source 12
SGF 2017 Predictive Models: Storing, Scoring, and Evaluating Matthew Duchnowski source 10
SGF 2017 PROC LOGISTIC: Using New SAS 9.4 Features for Cumulative Logit Models with Partial Proportional Odds Paul Hilliard source
zip
pdf		 16
SGF 2017 Property and Casualty Insurance Predictive Analytics in SAS Mei Najim source 17
SGF 2017 Real AdaBoost: Boosting for Credit Scorecards and Similarity to WOE Logistic Regression Paul Edwards source 12
SGF 2017 SAS Macros for Binning Predictors with a Binary Target Bruce S. Lund source 13
SGF 2017 Step Up Your Statistical Practice with Today's SAS/STAT Software Robert N. Rodriguez,
Phil Gibbs		 source 19
SGF 2017 Streamline Your Workflow: Integrating SAS, LaTeX, and R into a Single Reproducible Document Lucy D'Agostino McGowan source 10
SGF 2017 The Flamingos: NFL Data Analytics For A New Era Jonah Muresan,
Daniel Savage,
Gus Moir		 source 14
SGF 2017 The Three Amigos Factors: Determining Term Deposit Purchases: How a Bank Can Get Other People's Money Gina Colaianni,
Bogdan Gadidov,
Matthew Mitchell		 source 59
SGF 2017 The Truth Is Out There: Leveraging Census Data Using PROC SURVEYLOGISTIC Richard Dirmyer source 5
SGF 2017 Use Machine Learning to Discover Your Rules David R. Duling source 10
SGF 2017 Using a Population Average Model to Investigate the Success of a Customer Retention Strategy Colleen E. McGahan source 19
SGF 2017 Using Segmentation to Build More Powerful Models with SAS Visual Analytics Darius S. Baer,
Sam Edgemon		 source 18
SGF 2017 Using Text Analysis to Improve the Quality of Scoring Models with SAS Enterprise Miner Piotr Małaszek source 11
SGF 2017 Using the LOGISTIC or SURVEYLOGISTIC Procedure and Weighting of Public-Use Data in the Classroom Tyler C. Smith,
Besa Smith		 source 9
SGF 2017 Weight of Evidence Coding for the Cumulative Logit Model Bruce S. Lund source 15
SGF 2017 What Statisticians Should Know about Machine Learning David Richard Cutler source 13
SESUG 2016 A Data Mining Approach to Predict Student-at-risk Youyou Zheng,
Thanuja Sakruti,
Abhishek Uppalapati,
Sivakumar Jaganathan		 source 12
SESUG 2016 A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius Matching Within source 11
SESUG 2016 Analyzing non-normal data with categorical response variables Niloofar Ramezani,
Allameh Tabataba'i		 source 9
SESUG 2016 Factors of Multiple Fatalities in Car Crashes Bill Bentley,
Gina Colaianni,
Kennedy Onzere,
Cheryl Joneckis,
Xuelei (Sherry) Ni		 source 62
SESUG 2016 Know your Interest Rate Anirban Chakraborty,
Goutam Chakraborty		 source 16
SESUG 2016 PROC LOGISTIC: Traps for the unwary Peter L. Flom source 7
SESUG 2016 Using SAS/QC to Design Optimal Experimental Designs in Consumer Lending Jonas V. Bilenas source 12
PHUSE EU 2016 Considerations in the Use of Propensity Scoresin Observational Studies Lawrence Rasouliyan,
Estel Plana		 source
presentation source		 17
MWSUG 2016 A Demonstration of Various Models Used in a Key Driver Analysis Steven M. LaLonde 17
MWSUG 2016 An Innovative Method of Customer Clustering Brian Borchers 6
MWSUG 2016 Automating the Process of Generating Publication Quality Regression Tables through SAS Base Programming Ji Qi 16
MWSUG 2016 Fitting a Cumulative Logistic Regression Model Shana Kelly 4
MWSUG 2016 Identifying the factors responsible for loan defaults and classification of customers using SAS Enterprise Miner Juhi Bhargava,
Prashanth Reddy Musuku		 13
MWSUG 2016 Is My Model the Best? Methods for Exploring Model Fit Deanna Naomi Schreiber-Gregory 7
MWSUG 2016 Property and Casualty Insurance Predictive Analytics in SAS Mei Najim 17
MWSUG 2016 The Practice of Credit Risk Modeling for Alternative Lending Keith Shields,
Bruce S. Lund		 8
MWSUG 2016 To be two or not be two, that is a LOGISTIC question Robert G. Downer 10
MWSUG 2016 Use Multi-Stage Model to Target the Most Valuable Customers Chao Xu,
Jing Ren,
Hongying Yang		 10
MWSUG 2016 Using SAS to Generate p-Values with Monte Carlo Simulation Brandy R. Sinco,
Edith Kieffer,
Michael S. Spencer,
Michael Woodford,
Gloria Palmisano,
Gretchen Piatt,
Michele Heisler		 14
MWSUG 2016 Weight of Evidence Coding and Binning of Predictors in Logistic Regression Bruce S. Lund 16
WUSS 2016 An Animated Guide: Incremental Response Modeling in Enterprise Miner Russell M. Lavery source 19
WUSS 2016 Demystifying the CONTRAST and ESTIMATE Statement Hector Lemus source 13
WUSS 2016 Employing Latent Analyses in Longitudinal Studies: An Exploration of Independently Developed SAS Procedures Deanna Naomi Schreiber-Gregory source 14
WUSS 2016 Hierarchical Generalized Linear Models for Behavioral Health Risk-Standardized 30-Day and 90-Day Readmission Rates Allen Hom source
pptx		 20
WUSS 2016 How to analyze correlated and longitudinal data? Niloofar Ramezani source 8
WUSS 2016 Selection Bias: How Can Propensity Score Utilization Help Control For It? Deanna Naomi Schreiber-Gregory source 13
PharmaSUG 2016 Latent Structure Analysis Procedures in SAS Deanna Naomi Schreiber-Gregory source 8
PharmaSUG 2016 Programming Support for Exposure-Response Analysis in Oncology Drug Development Peter Lu,
Hong Yan		 source 8
PharmaSUG 2016 ROC Curve: Making way for correct diagnosis Manoj Pandey,
Abhinav Jain		 source 12
PharmaSUG 2016 Simulation of Data using the SAS System, Tools for Learning and Experimentation Kevin R. Viel,
Bevan K. Youse		 source 11
PharmaSUG 2016 Unequalslopes: Making life easier when the proportional odds assumption fails Matthew J. Wiedel source 11
SGF 2016 A Property and Casualty Insurance Predictive Modeling Process in SAS Mei Najim source 14
SGF 2016 Analysis of Grades for University Students Using Administrative Data and the IRT Procedure Sara Armandi source 13
SGF 2016 Analyzing Non-Normal Binomial and Categorical Response Variables under Varying Data Conditions Niloofar Ramezani source 16
SGF 2016 Analyzing the Hospital Episode Statistics Data Set: How Much Does SAS Help? Violeta Balinskaite source 9
SGF 2016 Application of Data Mining Techniques in Improving Breast Cancer Diagnosis Josephine Akosa source
presentation		 10
SGF 2016 Applying Frequentist and Bayesian Logistic Regression to MOOC data in SAS: a Case Study Yan Zhang,
Ryan Baker,
Yoav Bergner		 source
presentation		 7
SGF 2016 Best Practices for Resource Management in Hadoop James Kochuba source 10
SGF 2016 Comparative Regional Analysis of Bacterial Pneumonia Readmission Patients in the Medicare Category Heramb Joshi,
Goutam Chakraborty,
William Paiva,
Aditya Sharma		 source 12
SGF 2016 Correcting the Quasi-complete Separation Issue in Logistic Regression Models Xinghe Lu source 10
SGF 2016 Credit Card Interest and Transactional Income Prediction with an Account-Level Transition Panel Data Model Using SAS/STAT and SAS/ETS Software Denys Osipenko source 20
SGF 2016 Credit Risk Modeling in a New Era Wei Chen,
Martim Rocha,
Jimmy Skoglund		 source 16
SGF 2016 Customer Lifetime Value Modeling Vadim Pliner source
presentation		 4
SGF 2016 Disparities in the Receipt of Cardiac Revascularization Procedures C. Perry Brown,
Jontae Sanders		 source 13
SGF 2016 Do SAS High-Performance Statistics Procedures Really Perform Highly ? A Comparison of HP and Legacy Procedures Diep T. Nguyen,
Sean Joo,
Anh P. Kellermann,
Jeffrey D. Kromrey,
Jessica Montgomery,
Patricia Rodríguez de Gil,
Yan Wang		 source
presentation		 10
SGF 2016 Enhanced Segmentation Using SAS Visual Analytics and SAS Visual Statistics Darius S. Baer,
Suneel Grover		 source 19
SGF 2016 Evaluation of a Customer's Life Cycle Time-to-Offer Cross-sales in a Bank, Based on the Behavior Score Using Logit Models & DTMC in SAS Oscar Javier Cortés Arrigui,
Miguel Angel Diaz Rodriguez		 source 13
SGF 2016 Exact Logistic Models for Nested Binary Data in SAS Kyle Irimata,
Jeffrey Wilson		 source 6
SGF 2016 Factors of Multiple Fatalities in Car Crashes Bill Bentley,
Gina Colaianni,
Cheryl Joneckis,
Sherry Ni,
Kennedy Onzere		 source 60
SGF 2016 Finding and Evaluating Multiple Candidate Models for Logistic Regression Bruce S. Lund source
zip		 17
SGF 2016 Latent Structure Analysis Procedures in SAS Deanna Naomi Schreiber-Gregory source 8
SGF 2016 Limit of Detection (LoD) Estimation Using Parametric Curve Fitting to (Hit) Rate Data: The LOD_EST SAS Macro Jesse Albert Canchola,
Pari Hemyari		 source
zip		 10
SGF 2016 Lyric Complexity and Song Popularity: Analysis of Lyric Composition and Relation among Billboard Top 100 Songs John Harden,
Yang Gao,
Vojtech Hrdinka,
Chris Linn		 source 15
SGF 2016 Make Predictions More Predictive: Lift Retail Marketing Campaign Response by Blending Logistic Regression and the Cox Proportional Hazards Model Hsin-Yi Wang source 10
SGF 2016 Missing Values: They Are NOT Nothing Justin Jia,
Shan Shan Lin		 source 19
SGF 2016 Predicting Human Activity Sensor Data Using an Auto Neural Model with Stepwise Logistic Regression Inputs Venkata Vennelakanti source
presentation		 10
SGF 2016 Probability Density for Repeated Events Bruce S. Lund source 16
SGF 2016 Reducing and Screening Redundant Variables in Logistic Regression Models Using SAS/STAT Software and SAS Enterprise Miner Xinghe Lu source 10
SGF 2016 Restricted Cubic Spline Regression: A Brief Introduction Ruth Croxford source 5
SGF 2016 SAS Macro for Generalized Method of Moments Estimation for Longitudinal Data with Time-Dependent Covariates Katherine Cai,
Jeffrey Wilson		 source 9
SGF 2016 SAS Visual Analytics and SAS/ACCESS Interface for Hadoop: Improving Efficiency and Increasing Analyst Satisfaction Rosie Poultney source 10
SGF 2016 SAS Visual Statistics 8.1: The New Self-Service, Easy Analytics Experience Xiangxiang Meng,
Donald R. Chapman,
Cheryl LeSaint		 source 11
SGF 2016 Using Administrative Databases for Research: Propensity Score Methods to Adjust for Bias Ruth Croxford source 10
SGF 2016 Using Propensity Score Analyses to Adjust for Selection Bias: A Study of Adolescent Mental Illness and Substance Use Deanna Naomi Schreiber-Gregory source 7
MWSUG 2015 A Macro for Systematic Treatment of Special Values in Weight of Evidence Variable Transformation Chaoxian Cai 9
MWSUG 2015 A Property & Casualty Insurance Predictive Modeling Process in SAS Mei Najim 14
MWSUG 2015 A Simple Adjustment for Selection Bias Through Use of Propensity Scoring Deanna Naomi Schreiber-Gregory 24
MWSUG 2015 Fitting and Evaluating Logistic Regression Models Bruce S. Lund 26
MWSUG 2015 Get the highest bangs for your marketing bucks using Incremental Response Models in SAS Enterprise Miner Delali Agbenyegah 8
MWSUG 2015 SAS Software as an Essential Tool in Statistical Consulting and Research Erika Larsen,
Timothy E. O'Brien		 13
MWSUG 2015 Utilizing Propensity Score Analyses to Adjust for Selection Bias: A Study of Adolescent Mental Illness and Substance Use Deanna Naomi Schreiber-Gregory 7
PHUSE EU 2015 Bias Reduction in Observational Cohort Studies - Obtaining Matched Cohorts Using Propensity Scores Shelley Fordred,
Victor Kiri		 source
presentation source		 8
PHUSE EU 2015 How to Reduce Bias in the Estimates of Count Data Regression Ashwini Joshi,
Sumit Singh		 source
presentation source		 10
PHUSE EU 2015 Only Time Will Tell? - Identification of Predictors for Binary Outcomes in Longitudinal Designs [presentation] Knut Mueller source 1
SESUG 2015 Adaptive Fractional Polynomial Modeling in SAS George J. Knafl source 10
SESUG 2015 Can Fit Indices Yielded from the SAS GLIMMIX Procedure Select the Accurate Q-matrix? Yan Wang,
Yi-Hsin Chen,
Isaac Y. Li,
Chunhua Cao		 source 8
SESUG 2015 Comparing Results from Cox Proportional Hazards Models using SUDAAN and SAS Survey Procedures to a Logistic Regression Model for Analysis of Influenza Vaccination Coverage Yusheng Zhai,
K. E. Kahn,
A. O'Halloran		 source 13
SESUG 2015 High-Performance Procedures in SAS 9.4: Comparing Performance of HP and Legacy Procedures Jessica Montgomery,
Sean Joo,
Anh P. Kellermann,
Jeffrey D. Kromrey,
Diep T. Nguyen,
Patricia Rodríguez de Gil,
Yan Wang		 source 10
SESUG 2015 How Latent Structure Analysis Can Improve the Fit of a Regression Model Deanna Naomi Schreiber-Gregory source 11
SESUG 2015 Integrating PROC REG and PROC LOGISTIC for Collinearity Examination, Sample Scoring and Model Evaluation Alec Zhixiao Lin source 11
SESUG 2015 Optimizing Pilot Connection Time Using PROC REG and PROC LOGISTIC Andrew Hummel,
Shevawn Christian		 source 4
SESUG 2015 Predictive Modeling Using SAS Visual Statistics: Beyond the Prediction Xiangiang Meng,
Wayne Thompson,
Jennifer Ames		 source 12
SESUG 2015 Prescription Opioid Use in the U.S. in 2012: Characterizing Sustained vs. Infrequent Use Using the Medical Expenditure Panel Survey Monika C. Lemke source 16
SESUG 2015 Probability Density for Repeated Events Bruce S. Lund source 16
WUSS 2015 Exposure-Response Plots Using SAS Janette Garner source 8
WUSS 2015 Latent Analysis Applications for Regression Models Deanna Naomi Schreiber-Gregory source 11
WUSS 2015 Managing and Monitoring Statistical Models Nathaniel Derby source 13
WUSS 2015 ODS Output Data Sets That Work for You Stuart D. Long,
Edward Heaton,
Daniel R. Scharf		 source 29
WUSS 2015 SAS/STAT 14.1: Methods for Massive, Missing, or Multifaceted Data Maura E. Stokes source 25
PharmaSUG CN 2015 Why Statistical Analysis Plan (SAP) should be comprehensive. Riddhi Merchant,
Ranjith Prayankotveettil		 6
PharmaSUG 2015 A General SAS Macro to Implement Optimal N:1 Propensity Score Matching Within a Maximum Radius Kathy Hardis Fraeman source 12
PharmaSUG 2015 How To Use Latent Analyses Within Survey Data Can Be Valuable Additions to Any Regression Model Deanna Naomi Schreiber-Gregory source 9
PharmaSUG 2015 Medication Adherence in Cardiovascular Disease: Generalized Estimating Equations in SAS Erica Goodrich,
Daniel Sturgeon		 source 8
SGF 2015 A Model Family for Hierarchical Data with Combined Normal and Conjugate Random Effects [presentation] Geert Molenberghs source 54
SGF 2015 A New Method of Using Polytomous Independent Variables with Many Levels for the Binary Outcome of Big Data Analysis John Gao,
Jesse Harriott,
Lisa Pimentel		 source 7
SGF 2015 Addressing AML Regulatory Pressures by Creating Customer Risk Rating Models with Ordinal Logistic Regression Edwin Rivera,
Jim West		 source 12
SGF 2015 An Innovative Method of Customer Clustering Brian Borchers,
Ashlie Ossege		 source 6
SGF 2015 Analysis of Survey Data Using the SAS SURVEY Procedures: A Primer Patricia A. Berglund source 19
SGF 2015 Applied Analytics in Festival Tourism: A Case Study of Intention-to-Revisit Prediction in an Annual Local Food Festival in Thailand Jongsawas Chongwatpol,
Thanathorn Vajirakachorn		 source 13
SGF 2015 Credit Card Holders' Behavior Modeling: Transition Probability Prediction with Multinomial and Conditional Logistic Regression in SAS/STAT Denys Osipenko,
Jonathan Crook		 source 13
SGF 2015 Data Summarization for a Dissertation: A Grad Student How-To Paper Elisa L. Priest,
Ashley Collinsworth		 source 8
SGF 2015 Forest Plotting Analysis Macro %FORESTPLOT Jeffrey P. Meyers,
Qian Shi		 source 18
SGF 2015 Getting Your Hands on Reproducible Graphs Rebecca Ottesen,
Leanne Goldstein		 source 19
SGF 2015 How Latent Analyses within Survey Data Can Be Valuable Additions to Any Regression Model Deanna Naomi Schreiber-Gregory source 9
SGF 2015 How to Implement Two-Phase Regression Analysis to Predict Profitable Revenue Units [presentation] Shrey Tandon source 15
SGF 2015 How to Use SAS for GMM Logistic Regression Models for Longitudinal Data with Time-Dependent Covariates Katherine Cai source 8
SGF 2015 Identifying Factors Associated with High-Cost Patients Jeff Dang source
zip		 12
SGF 2015 Improving the Performance of Two-Stage Modeling Using the Association Node of SAS Enterprise Miner 12.3 Girish Shirodkar,
Goutam Chakraborty,
Ankita Chaudhari		 source 9
SGF 2015 Lasso Regularization for Generalized Linear Models in Base SAS Using Cyclical Coordinate Descent Robert Feyerharm source 10
SGF 2015 Modeling to Improve the Customer Unit Target Selection for Inspections of Commercial Losses in the Brazilian Electric Sector: The case of CEMIG Sergio Henrique Ribeiro,
Iguatinan Monteiro		 source 7
SGF 2015 Multinomial Logistic Model for Long-Term Value Bruce S. Lund source 16
SGF 2015 Multiple Imputation Using the Fully Conditional Specification Method: A Comparison of SAS, Stata, IVEware, and R Patricia A. Berglund source 17
SGF 2015 Multiple Ways to Detect Differential Item Functioning in SAS Yan Zhang source 9
SGF 2015 Predicting Transformer Lifetime Using Survival Analysis and Modeling Risk Associated with Overloaded Transformers Using SAS Enterprise Miner 12.1 Balamurugan Mohan,
Goutam Chakraborty		 source 18
SGF 2015 Predictive Modeling Using SAS Visual Statistics: Beyond the Prediction Xiangxiang Meng,
Jennifer Ames,
Wayne Thompson		 source 12
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 Risk-Adjusting Provider Performance Utilization Metrics Stephanie Poley source 9
SGF 2015 SAS/STAT 14.1: Methods for Massive, Missing, or Multifaceted Data Maura E. Stokes,
Bob Rodriguez		 source 25
SGF 2015 Selection and Transformation of Continuous Predictors for Logistic Regression Bruce S. Lund source 16
SGF 2015 Survival Analysis with Survey Data Joseph C. Gardiner source 17
SGF 2015 The Comparative Analysis of Predictive Models for Credit Limit Utilization Rate with SAS/STAT Denys Osipenko,
Jonathan Crook		 source 19
SGF 2015 The Spatio-Temporal Impact of Urgent Care Centers on Physician and ER Use Laurel Trantham source 6
SGF 2015 Using PROC SURVEYREG and PROC SURVEYLOGISTIC to Assess Potential Bias Lucy D'Agostino McGowan,
Alice Toll		 source 8
SGF 2015 Using SAS to Combine Regression and Time Series Analysis on U.S. Financial Data to Predict the Economic Downturn Avinash Kalwani,
Nishant Vyas		 source 6
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
SGF 2015 Using SAS to Increase Profitability through Cluster Analysis and Simplicity: Follow the Money [presentation] Sherrie Rodriguez source 9
SGF 2015 When Two Are Better Than One: Fitting Two-Part Models Using SAS Laura Kapitula source 9
SGF 2015 Why Do People Choose to Drive Over Other Travel Modes? Modelling Driving Motivation with SAS Jun Neoh source 8
SCSUG 2014 Building Better Credit Scores using Reject Inference and SAS Steve Fleming 15
SCSUG 2014 Selection and Transformation of Continuous Predictors for Logistic Regression Bruce S. Lund 13
SESUG 2014 %DISCIT Macro: Pre-screening continuous variables for subsequent binary logistic regression analysis through visualization Mohamed S. Anany source 6
SESUG 2014 Don't be binary! Tales of Non-binary Categorical Regression Charlotte Baker source 6
SESUG 2014 Expanding the Use of Weight of Evidence and Information Value to Continuous Dependent Variables for Variable Reduction and Scorecard Development Alec Zhixiao Lin,
Tung-Ying Hsieh		 23
SESUG 2014 Identifying Superutilizers in the Medicaid Population - A Simple and Powerful technique Aran Canes 7
SESUG 2014 Impact of Q-matrix Misspecification on Cognitive Attribute Estimation in the Crossed Random Effects LLTM Model with the SAS GLIMMIX Procedure Yi-Hsin Chen 11
SESUG 2014 Making Comparisons Fair: How LS-Means Unify the Analysis of Linear Models Weijie Cai source 22
SESUG 2014 Modeling Cognitive Processes of Learning with SAS Procedures Isaac Y. Li,
Yi-Hsin Chen,
Chunhua Cao,
Yan Wang		 source 11
SESUG 2014 Text Analytics using High Performance SAS Text Miner Edward R. Jones source 7
SESUG 2014 To foam or not to foam: A survival analysis of the foam head that forms when a soda is poured Kate Phillips source 16
PHUSE EU 2014 Biomarker as Essential Part of Clinical Development Renuka Chinthapally source
presentation source		 7
PHUSE EU 2014 Inverse Probability of Censoring Weighting for Selective Crossover in Oncology Clinical Trials José Luis Jiménez Moro,
Javier Gómez Garcia		 source
presentation source		 5
PHUSE EU 2014 Population Segmentation in a Healthcare Environment MaryAnne DePesquo source
presentation source		 5
PHUSE EU 2014 SAS Visual Statistics [presentation] Andy Bayliss source 18
MWSUG 2014 An Object Oriented Framework for Simulations in base SAS Michael C. Frick 15
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 How To Use Latent Analyses of Survey Data in a Logistic Regression Model Deanna Naomi Schreiber-Gregory 9
MWSUG 2014 Preparing Interaction Variables for Logistic Regression Bruce S. Lund 9
MWSUG 2014 Selection and Transformation of Continuous Predictors for Logistic Regression Bruce S. Lund 12
MWSUG 2014 Text Analytics in High Performance SAS and SAS Enterprise Miner Edward R. Jones 16
WUSS 2014 Fuzzy Bootstrap Matching Dale Harrington source 9
WUSS 2014 Practice Pattern Instrumental Variables for Comparative Effectiveness Carol Conell,
Alexander C. Flint		 source 15
WUSS 2014 Using PROC FACTOR for variable inclusion in PROC SURVEYLOGISTIC: A Case Study Deanna Naomi Schreiber-Gregory source 35
WUSS 2014 Using SAS and the Centers for Disease Control and Prevention 2010 Natality Data to Estimate the Effect Size of Prenatal Visits on Preterm Birth Melissa Borelli source 10
PharmaSUG 2014 %IC_LOGISTIC: A SAS Macro to Produce Sorted Information Criteria (AIC/BIC) List for PROC LOGISTIC for Model Selection Qinlei Huang,
Liang Zhu		 source 12
PharmaSUG 2014 A Mental Health and Risk Behavior Analysis of American Youth Using PROC FACTOR and SURVEYLOGISTIC Deanna Naomi Schreiber-Gregory source 23
PharmaSUG 2014 Clinical Trial Data Integration: The Strategy, Benefits, and Logistics of Integrating Across a Compound Natalie Reynolds,
Keith Hibbetts		 source 12
PharmaSUG 2014 Handling with missing data in clinical trials for time-to-event variables Giulia Tonini,
Simona Scartoni,
Angela Capriati		 source 9
PharmaSUG 2014 Healthcare Data Manipulation and Analytics Using SAS Lumin Shen,
Jane Lu		 source 11
PharmaSUG 2014 Methodology for Non-Randomized Clinical Trials: Propensity Score Analysis Dan Conroy source 5
PharmaSUG 2014 Survival Analysis Approaches and New Developments using SAS Jane Lu,
David Shen		 source 13
PharmaSUG 2014 The path less trodden - PROC FREQ for ODDS RATIO Manjusha Gondil,
Linga Reddy Baddam		 source 4
PharmaSUG 2014 Using SAS to Calculate and Compare Adjusted Relative Risks, Odds Ratios, and Hazard Ratios Besa Smith,
Tyler C. Smith		 source 23
SGF 2014 A Mental Health and Risk Behavior Analysis of American Youth Using PROC FACTOR and SURVEYLOGISTIC Deanna Naomi Schreiber-Gregory source 23
SGF 2014 A Risk Score Calculator for Short-Term Morbidity Following Hip Fracture Surgery Yubo Gao source 5
SGF 2014 A SAS Macro for Complex Sample Data Analysis Using Generalized Linear Models Paulo Henrique Dourado da Silva,
Alan Ricardo da Silva		 source 19
SGF 2014 A Stepwise Algorithm for Generalized Linear Mixed Models Nagaraj Neerchal,
Jorge Morel,
Xuang Huang,
Alain Moluh		 source 11
SGF 2014 Association of Socio-Emotional Factors: Religious Affiliation, Depression to Suicidal Tendency Among Adolescent Girls Sesha Sai Ega,
Chandra Shekar Pulipati,
Venkata Rachapudi		 source 6
SGF 2014 Building Gamer Segmentation in the Credit Card Industry Using SAS Enterprise Guide Yang Ge source 10
SGF 2014 Customer Profiling for Marketing Strategies in a Healthcare Environment MaryAnne DePesquo source 7
SGF 2014 Dining with the Data: The Case of New York City and Its Restaurants Pruthvi Bhupathiraju Venkata,
Goutam Chakraborty		 source 7
SGF 2014 Examples of Logistic Modeling with the SURVEYLOGISTIC Procedure Rob Agnelli source 17
SGF 2014 Got Randomness? SAS for Mixed and Generalized Linear Mixed Models David A. Dickey source 12
SGF 2014 Healthcare Services Data Distribution, Transformation, and Model Fitting Dawit Mulugeta,
Jason Greenfield,
Tison Bolen,
Lisa Conley		 source 22
SGF 2014 Introduction to SAS Decision Management Steve Sparano,
Charlotte Crain,
David R. Duling		 source 18
SGF 2014 Item Response Theory: What It Is and How You Can Use the IRT Procedure to Apply It Xinming An,
Yiu-Fai Yung		 source 14
SGF 2014 Leveraging Publicly Available Data in the Classroom Using SAS PROC SURVEYLOGISTIC Tyler C. Smith,
Besa Smith		 source 18
SGF 2014 Making Comparisons Fair: How LS-Means Unify the Analysis of Linear Models Weijie Cai source 22
SGF 2014 Managing Opt-Out Risk Jia Lei source 4
SGF 2014 Measures of Fit for Logistic Regression Paul D. Allison source 12
SGF 2014 Medical Scoring for Breast Cancer Recurrence Nurul Husna Jamian,
Yap Bee Wah,
Nor Aina Emran		 source 13
SGF 2014 Modeling Fractional Outcomes with SAS Wensui Liu,
Jason Xin		 source 17
SGF 2014 Parameter Estimation of Cognitive Attributes Using the Crossed Random-Effects Linear Logistic Test Model with PROC GLIMMIX Chunhua Cao,
Yan Wang,
Yi-Hsin Chen,
Isaac Y. Li		 source 12
SGF 2014 Predicting a Child Smoker Using SAS Enterprise Miner 12.1 [presentation] Jin Ho Jung,
Gaurav Pathak,
Goutam Chakraborty		 source 3
SGF 2014 Regression Analysis of Duration and Severity Data: New Capabilities with SAS Software Joseph C. Gardiner source 19
SGF 2014 Researching Individual Credit Rating Models Colin Nugteren source 12
SGF 2014 The Role of Customer Response Models in Customer Solicitation Center's Direct Marketing Campaign Arun Mandapaka,
Amit Kushwah,
Goutam Chakraborty		 source 12
SGF 2014 Using SAS to Evaluate Patient-Directed Quality of Care Interventions Scott P. Leslie source 10
SGF 2014 Validating Self-Reported Survey Measures Using SAS Sarah Lyons,
Kimberly Kaphingst,
Melody S. Goodman		 source 6
WUSS 2013 A Tutorial on PROC LOGISTIC Arthur X. Li source 19
WUSS 2013 Estimating the Impact of Lost to Follow-up on Breast Cancer Patients' Disease-free Survival Debbie Yan Qun Huang source 7
WUSS 2013 Matching with Hash Objects Gregory Watson source 6
WUSS 2013 Optimism of Best Subset Selection by AIC/BIC for Prognostic Model Building Yinghui Miao,
Irena Stijacic Cenzer,
Katharine Kirby,
W. John Boscardin		 source 13
WUSS 2013 Proper Estimation of Relative Risk Using PROC GENMOD in Population Studies Kechen Zhao source 14
WUSS 2013 The Association of Morbid Obesity with Mortality and Coronary Revascularization among Patients with Acute Myocardial Infarction using ARRAYS, PROC FREQ and PROC LOGISTIC" Ashwini Erande,
Shaista Malik		 source 11
WUSS 2013 Tools for Imputing Missing Data Taylor Lewis source 16
WUSS 2013 User-Specified Likelihood Expressions Using NLMIXED and the GENERAL Statement Melissa McTernan,
Shelley A. Blozis		 source 4
WUSS 2013 Using PROC NLMIXED to Fit Correlated Residual Error Structures in Nonlinear Mixed Models Shelley A. Blozis,
Jeffrey Harring		 source 5
WUSS 2013 Weighting Multiple Years to Identify Trends: A Logistic Regression of Public Use Data Marie Boman-Davis source 5
SCSUG 2013 Hypertension Management in Hypertensive Adults with Diabetes in Primary Care setting Pratik P. Rane,
Archita Bhansali,
Ruta Sawant,
Rajender R. Aparasu		 source 12
SCSUG 2013 Preparing Interaction Variables for Logistic Regression Bruce S. Lund source 14
SCSUG 2013 Using SAS to Build Customer Level Datasets for Predictive Modeling Scott M. Shockley source 14
SESUG 2013 Area under a Receiving Operating Characteristic (ROC) Curve: comparing parametric estimation, Monte Carlo simulation and numerical integration Receiver Operating Characteristic source 16
SESUG 2013 Comparing SAS PROC MI and IVEware Callable Software Bruno Vizcarra,
Amang Sukasih		 source 11
SESUG 2013 Dealing with Missing Data for Credit Scoring Steve Fleming source 7
SESUG 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 16
SESUG 2013 Introducing the New ADAPTIVEREG Procedure for Adaptive Regression Warren F,
Weijie Cai		 source 21
SESUG 2013 SAS Macros to Conduct Common Biostatistical Analyses and Generate Reports Dana Nickleach,
Yuan Liu,
Adam Shrewsberry,
Kenneth Ogan,
Sungjin Kim		 source 8
PHUSE EU 2013 Bayesian Statistics: Concept and Bayesian Capabilities in SAS Mark Janssens source
presentation source		 15
PHUSE EU 2013 Overview of Receiver Operating Characteristic (ROC) Curves in SAS PROC LOGISTIC Paul Constantin source
presentation source		 1
PHUSE EU 2013 Using PROC GENMOD to Analyses Ratio to Placebo in Change of Dactylitis Imgard Hollweck source
presentation source		 9
MWSUG 2013 A Tutorial on PROC LOGISTIC Arthur X. Li 19
MWSUG 2013 An Analysis of Risk Behavior Trends and Mental Health in American Youth Using PROC SURVEYLOGISTIC Deanna Naomi Schreiber-Gregory 15
MWSUG 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey 16
MWSUG 2013 Ideas and Examples in Generalized Linear Mixed Models David A. Dickey 12
MWSUG 2013 Improved Interaction Interpretation: Application of the EFFECTPLOT statement and other useful features in PROC LOGISTIC Robert G. Downer 8
MWSUG 2013 Information Value Statistic Bruce S. Lund,
David Brotherton		 18
MWSUG 2013 Logistics Performance Metrics using SAS Macros Michael C. Frick 9
MWSUG 2013 Managing and Monitoring Statistical Models Nathaniel Derby 13
MWSUG 2013 Residential Energy Efficiency and the Principal-Agent Problem Ryan Anderson 11
MWSUG 2013 SAS PROC REPORT: How It Helps Improving College Student Retention Rate Harjanto Djunaidi,
Monica Djunaidi		 14
MWSUG 2013 Statistical Models for Proportional Outcomes Wensui Liu,
Kelly Zhao		 17
NESUG 2013 An Animated Guide: An Introduction to Neural Nets: Russell M. Lavery 12
NESUG 2013 Proc SurveyCorr Jessica R. Hampton 7
NESUG 2013 Understanding the outliers in healthcare expenditure data Gandhi Bhattarai 15
PharmaSUG 2013 Combining Analysis Results from Multiply Imputed Categorical Data Bohdana Ratitch,
Ilya Lipkovich,
Michael O'Kelly		 source 19
SGF 2013 A Data-Driven Analytic Strategy for Increasing Yield and Retention at Western Kentucky University Using SAS Enterprise BI and SAS Enterprise Miner Matt T. Bogard source 12
SGF 2013 A Flexible Method to Apply Multiple Imputation Using SAS/IML Studio Lisa M. Lix,
Xue Yao		 source
pdf		 9
SGF 2013 A Flock of C-Stats, or Efficiently Computing Multiple Statistics for Hundreds of Variables Bruce S. Lund,
Steven Raimi		 source 11
SGF 2013 Are You Discrete? Patients' Treatment Preferences and the Discrete Choice Experiment Beeya Na,
Eric P. Elkin		 source 6
SGF 2013 Assessing Drug Safety with Bayesian Hierarchical Modeling Using PROC MCMC and JMP Richard C. Zink source 13
SGF 2013 Be Customer Wise or Otherwise: Combining Data Mining and Interactive Visual Analytics to Analyze Large and Complex Customer Resource Management (CRM) Data Aditya Misra,
Junyao Ji,
Kam Tin Seong		 source 11
SGF 2013 Bringing Churn Modeling Straight to the Source: SAS and Teradata In-Database Model Development Jonathan Wexler,
Karl Krycha		 source 9
SGF 2013 Build Prognostic Nomograms for Risk Assessment Using SAS Dongsheng Yang source
pdf		 7
SGF 2013 Demand Forecasting Using a Growth Model and Negative Binomial Regression Framework Cally Ong Yeru,
Michelle Cheong,
Murphy Choy		 source 17
SGF 2013 Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples Irena Stijacic Cenzer,
W. John Boscardin,
Katharine Kirby,
Yinghui Miao		 source 12
SGF 2013 Finding the Gold in Your Data: An Overview of Data Mining David A. Dickey source 16
SGF 2013 For All the Hats You Wear: SAS Enterprise Guide Has Got You Covered Chris Hemedinger source 8
SGF 2013 High Performance Statistical Modeling Bob Rodriguez,
Robert A. Cohen		 source 19
SGF 2013 Introducing the New ADAPTIVEREG Procedure for Adaptive Regression Warren F. Kuhfeld,
Weijie Cai		 source 18
SGF 2013 Joint Modeling of Mixed Outcomes in Health Services Research Joseph C. Gardiner source 16
SGF 2013 Managing and Monitoring Statistical Models Nathaniel Derby source 13
SGF 2013 Models for Ordinal Response Data Robin High source 16
SGF 2013 Ordinal Response Modeling with the LOGISTIC Procedure Bob Derr source 20
SGF 2013 Predicting Health Care Expenditures with the MCMC Procedure Gregory Watson source 12
SGF 2013 Predicting Women's Department Purchases in a Retail Store By Using the SEMMA Methodology Michael Soto source 12
SGF 2013 Statistical Analyses Using SAS Enterprise Guide Scott P. Leslie source 11
SGF 2013 The Value of Neighborhood Information in Prospect Selection Models: Investigating the Optimal Level of Granularity Dirk Van den Poel,
Philippe Baecke		 source 15
SGF 2013 Using the Boosting Technique to Improve the Predictive Power of a Credit Risk Model Alejandro Correa Bahnsen,
Andres F. Gonzalez,
Darwin A. Amezquita		 source 7
SGF 2013 Variable Reduction in SAS by Using Weight of Evidence and Information Value Alec Zhixiao Lin source 12
NESUG 2012 A Model for Extreme Stacking of Data at Endpoints of a Distribution Robert J. Gallop 8
NESUG 2012 Building a Predictive Model for 30-Day Inpatient Readmission Using PROC PHREG Klaus Lemke 13
NESUG 2012 Efficiently Screening Predictor Variables for Logistic Models Steven Raimi,
Bruce S. Lund		 13
SCSUG 2012 Institutional Research Intelligent: Go Beyond Reporting Harjanto Djunaidi source 9
SCSUG 2012 Net Present Value Model Approach Jingjing Qu source 7
SCSUG 2012 Predicting Students Enrollment Using SAS Harjanto Djunaidi source 9
SCSUG 2012 The Effect of Adding the Chronic Disease Score to the Charlson Comorbidity Score in Predicting Mortality in Type - II Diabetes Patients: An Application of Reclassification Measures Hemalkumar Mehta source 17
PHUSE EU 2012 Programming Challenges of Sampling Controls to Cases from the Dynamic Risk Sets in Nested Case Control Studies Victor Kiri source
presentation source		 8
PHUSE EU 2012 The Logic and Logistics of Logistic Regression, Including New Features in SAS 9.2 Lawrence Rasouliyan source
presentation source		 16
SESUG 2012 Multiple Imputation for Ordinal Variables: A Comparison of SUDAAN's PROC IMPUTE vs. PROC MI Multiple Imputation source 12
SESUG 2012 ODS Output Data Sets That Work for You Stuart D. Long,
Edward Heaton,
Daniel R. Scharf		 source 29
MWSUG 2012 An Analysis of Diabetes Risk Factors Using Data Mining Approach Akkarapol Sa-ngasoongsong,
Jongsawas Chongwatpol		 11
MWSUG 2012 Collapsing Levels of Predictor Variables for Logistic Regression and Weight of Evidence Coding Bruce S. Lund,
Steven Raimi		 13
MWSUG 2012 Direct Marketing Profit Model Bruce S. Lund 8
MWSUG 2012 Methods for Interaction Detection in Predictive Modeling Using SAS Douglas Thompson 13
MWSUG 2012 Modeling Complex Survey Data Taylor Lewis 16
MWSUG 2012 Models for Ordinal Response Data Robin High 11
MWSUG 2012 ROC Analysis for the Evaluation of Ethyl Glucuronide (EtG) as a Long-term Alcohol Biomarker in Hair and Fingernails: Evidence from a Large College Drinking Study Daniel Fuhrmann,
Michael Fendrich,
Lisa Berger,
Charles Plate,
Doug Lewis,
Joseph Jones		 10
MWSUG 2012 So You Want to Install SAS? Rafi Sheikh 9
WUSS 2012 Basic Statistics with SAS Enterprise Guide: For Learners and Teachers and Everyone Else AnnMaria De Mars,
Chelsea Heaven		 source 20
WUSS 2012 Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples Irena Stijacic Cenzer,
Yinghui Miao,
Katharine Kirby,
W. John Boscardin		 source 9
WUSS 2012 I Want to Get Married Too: Assessing the Relationship between Marital Status and Education for African American Women Shanetta Robinson,
Tara Jiles,
AnnMaria De Mars		 source 11
WUSS 2012 Import Excel: Go Beyond The Wizard With Data-Driven Programming Renato G. Villacorte source 7
WUSS 2012 Up To Speed With Categorical Data Analysis Maura E. Stokes source 25
WUSS 2012 Weighting Adjustment Methods for Nonresponse in Surveys Taylor Lewis source 15
PharmaSUG CN 2012 An Illustration of Rate Difference Estimation with SAS in Logistic Regression Yun Guo,
Victor Wu,
Xinyu Xu,
Crystal Cheng		 ppt 7
PharmaSUG 2012 Are You Discrete? Patients' Treatment Preferences and the Discrete Choice Experiment Beeya Na,
Eric P. Elkin		 source 5
PharmaSUG 2012 Automated forward selection for Generalized Linear Models with Categorical and Numerical Variables using PROC GENMOD Manuel Sandoval source 5
PharmaSUG 2012 Making Your SAS Data JMP Through Hoops Mira Shapiro source 19
SGF 2012 An Experimental Comparison of Classification Techniques for Imbalanced Credit Scoring Data Sets Using SAS Enterprise Miner Iain L. Brown source 14
SGF 2012 Beyond Binary Outcomes: PROC LOGISTIC to Model Ordinal and Nominal Dependent Variables Eric P. Elkin source 8
SGF 2012 Black Box PROCs: PROC LOGISTIC Discovered Isabel H. Perry source 7
SGF 2012 Building WOE Binned LGD Scorecards using SAS Enterprise Miner Anthony J. van Berkel,
Naeem Siddiqi		 source 14
SGF 2012 Classification of Customers Textual Responses via Application of Topic Mining Anil K. Pantangi,
Goutam Chakraborty		 source 6
SGF 2012 Constructing a Credit Risk Scorecard Using Predictive Clusters Andres F. Gonzalez,
Alejandro Correa Bahnsen,
Ana C. Nieto,
Darwin A. Amezquita		 source 13
SGF 2012 Customizing ODS Graphical Output for SAS/STAT Procedures Yang Xiao,
Xiangxiang Meng		 source 9
SGF 2012 Enhanced Data Analysis Using SAS ODS Graphics and Statistical Graphics Patricia A. Berglund source 20
SGF 2012 Go Beyond the Wizard with Data-Driven Programming Renato G. Villacorte source 7
SGF 2012 Handling Missing Data by Maximum Likelihood Paul D. Allison source 21
SGF 2012 Including the Salesperson Effect in Purchasing Behavior Models Using PROC GLIMMIX Philippe Baecke,
Dirk Van den Poel		 source 11
SGF 2012 Introduction to Predictive Modeling with Examples David A. Dickey source 14
SGF 2012 Its About Time: Discrete Time Survival Analysis using SAS Enterprise Miner Sascha Schubert,
Susan Haller,
Taiyeong Lee		 source 15
SGF 2012 Making it Happen: Using SAS to Implement Business Intelligence and Analytics in Small and Midsize Organizations Richard R. Hallquist,
Dragos Coles,
Lorrie Gallego		 source 16
SGF 2012 Making Your SAS Data JMP Through Hoops Mira Shapiro source 19
SGF 2012 Modeling Heavy-Tailed Distributions in Healthcare Utilization by Parametric and Bayesian Methods Joseph C. Gardiner source 15
SGF 2012 Net Lift Model for Effective Direct Marketing Campaigns at 1800flowers.com Roman Kubiak source 5
SGF 2012 Propensity Score Analysis and Assessment of Propensity Score Approaches Using SAS Procedures Rheta E. Lanehart,
Patricia Rodríguez de Gil,
Eun Sook Kim,
Aarti P. Bellara,
Jeffrey D. Kromrey,
Reginald Lee		 source 11
SGF 2012 SAS Enterprise Guide with SAS On-Demand for Academics: Everything You Need to Know After ""It's Free"" AnnMaria De Mars source 20
SGF 2012 Use of Cutoff and SAS Code Nodes in SAS Enterprise Miner to Determine Appropriate Probability Cutoff Point for Decision Making with Binary Target Models Yogen Shah source 6
SGF 2012 Using SAS Enterprise BI and SAS Enterprise Miner to Reduce Student Attrition Matt T. Bogard,
Chris James,
Tuesdi Helbig,
Gina Huff		 source 10
SGF 2012 Using SAS to Extend Logistic Regression Dachao Liu source 8
SCSUG 2011 Using Proc Logistic, SAS Macros and ODS Output to evaluate the consistency of independent variables during the development of logistic regression models. An example from the retail banking industry Alexandros Vidras source 9
SCSUG 2011 When It's Not Random Chance: Creating Propensity Scores Using SAS EG Josie Brunner source 13
SESUG 2011 Analysis of a Binary Outcome Variable Using the FREQ and LOGISTIC Procedures Arthur X. Li source 18
SESUG 2011 Breastfeeding in Developing Countries: A Case Study of Nepalese Children Paerwen Paerhate,
George Mason		 source 4
SESUG 2011 JMP Analytics Applied in Diagnostic Radiology and Neurosurgery Trauma Research Melvin T. Alexander source 18
SESUG 2011 Linear Logistic Test Model: Using SAS to Simulate the Decomposition of Item Difficulty by Algorithm, Sample Size, Cognitive Component and Time to Convergence George MacDonald,
Jeffrey D. Kromrey		 source 11
SESUG 2011 On Deck: SAS/STAT 9.3 Maura E. Stokes,
Fang K. Chen		 source 20
SESUG 2011 Tailoring Logistic Regression Model Analyses with the ODDSRATIO Statement in PROC LOGISTIC Taylor Lewis source 8
WUSS 2011 Fitting latency models in epidemiological studies Bryan Langholz source 7
WUSS 2011 On Deck: SAS/STAT 9.3 Maura E. Stokes source 20
WUSS 2011 Statistical Analyses Using SAS Enterprise Guide R. Scott Leslie source 11
WUSS 2011 Up To Speed With Categorical Data Analysis Maura E. Stokes source 25
WUSS 2011 Using affinity Models for Buyer Behaviors Matthias Kehder source 2
WUSS 2011 Why Dummy Variable Makes You SMART, and How to Do it SEXY Brian Sun source 12
PHUSE EU 2011 Use of early longitudinal viral load as a surrogate to the virologic endpoint in Hepatitis C Igwebuike Enweonye source
presentation source		 12
MWSUG 2011 Before Logistic Modeling - A Toolkit for Identifying and Transforming Relevant Predictors Steven Raimi,
Bruce S. Lund		 18
MWSUG 2011 Evaluation of Novel Markers in Risk Prediction Kevin F. Kennedy 14
MWSUG 2011 On Deck: SAS/STAT 9.3 Maura E. Stokes,
Fang K. Chen,
Ying So		 20
MWSUG 2011 SAS Model Selection Macros for Complex Survey Data Using PROC SURVEYLOGISTIC/SURVEYREG Fang Wang,
Hee-Choon Shin		 27
MWSUG 2011 Using SAS to Extend Logistic Regression Dachao Liu 8
MWSUG 2011 Using SAS to Find the Best K for K-Nearest-Neighbor Classification Charlie Huang 14
NESUG 2011 An Optimal Search Process of "Eigen Knots" for Spline Logistic Regression John Gao,
Cheryl Caswell		 7
NESUG 2011 Implementing a Customer Lifetime Value Framework in SAS Krishna Mehta 8
NESUG 2011 On Deck: SAS/STAT 9.3 Maura E. Stokes 20
PharmaSUG 2011 Adjusting Analyses of Survey Results using a Predicted Probability of Response Rob Gately source 12
PharmaSUG 2011 Classification of frequent snoring from routine medical examinations using the NHANES Database Barry W. Row source 11
PharmaSUG 2011 Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterprise Miner Patricia B. Cerrito source 15
PharmaSUG 2011 CONTRAST and ESTIMATE Statements Made Easy: The LSMESTIMATE Statement Kathleen Kiernan,
Randy Tobias,
Phil Gibbs,
Jill Tao		 source 19
PharmaSUG 2011 FAQ: Issues with Efficacy Analysis of Clinical Trial Data Using SAS Sandeep Sawant,
Nehan Mohan		 source 5
PharmaSUG 2011 Formulas Calculating Risk Estimates and Testing for Effect Modification and Confounding Manojkumar B Agravat source 44
SGF 2011 An Application of SAS Simulation Studio: The Microsimulation of a Computer-Assisted Telephone Interviewing System Yves Belanger,
Kristen Couture,
Elisabeth Neusy		 source 8
SGF 2011 An Introduction to Survival Analysis Using Complex Sample Survey Data Patricia A. Berglund source 20
SGF 2011 Compare the Effectiveness of Different Methodologies in Prediction of Dichotomous Outcome Jimmy Fang source 8
SGF 2011 CONTRAST and ESTIMATE Statements Made Easy: The LSMESTIMATE Statement Kathleen Kiernan,
Randy Tobias,
Phil Gibbs,
Jill Tao		 source 19
SGF 2011 Exact Confidence Intervals for Risk-Adjusted Rates Versus Trouble in River City Ted Clay source 11
SGF 2011 Genetic Algorithm Optimization for Selecting the Best Architecture of a Multi-Layer Perceptron Neural Network: A Credit Scoring Case Alejandro Correa,
Andres F. Gonzalez,
Camilo Ladino		 source 8
SGF 2011 Logit Models in Practice: B, C, E, G, M, N, O, ... Joseph C. Gardiner,
Zhehui Luo		 source 20
SGF 2011 MCPOWER: a Flexible Macro Suite for Generating Monte Carlo Power Estimates for Linear, Logistic, and Poisson Regression Models Ken Kleinman source 5
SGF 2011 ODS Output Data Sets That Work for You Stuart D. Long,
Edward Heaton,
Daniel R. Scharf		 source 29
SGF 2011 On Deck: SAS/STAT 9.3 Maura E. Stokes,
Robert N. Rodriguez,
Funda Güneş		 source 20
SGF 2011 Patron Analytics in the Casino and Gaming Industry: How the House Always Wins Scott W. Sutton source 7
SGF 2011 Rasch Model and Beyond with PROC GLIMMIX: A Better Choice Teck Kiang Tan,
Wenshu Luo,
David Hogan		 source 12
SGF 2011 Taking Disease and Health Management Analytics into the Next Generation George S. Habek source 13
SGF 2011 The RANDOM Statement and More: Moving On with PROC MCMC Fang K. Chen source 21
SGF 2011 Unit Cost Accounting at Allianz Germany - Live Demo of SAS Financial Management Stephan Schnurr,
Andreas Bölscher,
Wolfgang Aleker		 source 9
SGF 2011 Up To Speed With Categorical Data Analysis Gary G. Koch,
Maura E. Stokes		 source 25
SGF 2011 Using PROC LOGISTIC to Estimate the Rasch Model Tianshu Pan,
Yumin Chen		 source 7
SGF 2011 Using SAS Procedures FREQ, GENMOD, LOGISTIC, and PHREG to Estimate Adjusted Relative Risks: A Case Study Jiming Fang source 11
SGF 2011 Visualizing Marginal Effects from Interactions in Generalized Linear Models Xiao Chen,
Brigid Wilson		 source 7
NESUG 2010 An Introduction to Implementing Propensity Score Matching with SAS Kathy Hardis Fraeman 12
NESUG 2010 Improve Customer Retention by Answering Who and Why? Ward Thomas 8
NESUG 2010 Multinomial and ordinal logistic regression using SAS Peter L. Flom 12
NESUG 2010 The Next Generation: SAS/STAT 9.22 Maura E. Stokes,
Robert N. Rodriguez,
Robert Cohen		 20
NESUG 2010 The Philadelphia Story (Or the LUG at the RUG) John J. Cohen,
Robert Schechter		 13
SCSUG 2010 Boost Your Confidence (Intervals) With SAS Peter Langlois source 42
SCSUG 2010 How Good is Your School Steve Fleming source 16
WUSS 2010 Answering Your Research Questions with Inferential Statistics Diana D. Suhr source 10
WUSS 2010 Data Visualization: "Every Picture Tells a Story" or "Putting Atomic Numbers on Your Socks"? AnnMaria De Mars source 11
WUSS 2010 Dummy Coding for Dummies Kathryn Martin source 10
WUSS 2010 Mix-and-Match: Mixed Models for Analyzing Binary Outcomes with Matched Samples Sarah Short source 9
WUSS 2010 The Next Generation: SAS/STAT 9.22 Robert N. Rodriguez source 20
MWSUG 2010 Adequacy and Information Values: Two Tools for Ranking Predictors in Generalized Linear Models Douglas Thompson 21
MWSUG 2010 An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 Patricia A. Berglund 12
MWSUG 2010 Evaluation of Methods to Determine Optimal Cut Points for Predicting Mortgage Default Valentin Todorov,
Douglas Thompson		 11
MWSUG 2010 Ideas and Examples in Generalized Linear Mixed Models David A. Dickey 12
MWSUG 2010 Medical Guidelines: Consensus Panel Versus Healthcare Analytics Patricia B. Cerrito 17
MWSUG 2010 PROC MIXED: Underlying Ideas with Examples David A. Dickey 19
MWSUG 2010 Simplifying Drug Discovery with JMP John A. Wass 14
MWSUG 2010 The Next Generation: SAS/STAT 9.22 Maura E. Stokes 20
SESUG 2010 A SAS Macro to Compute Added Predictive Ability of New Markers Predicting a Dichotomous Outcome Kevin F. Kennedy,
Michael J. Pencina		 source 13
SESUG 2010 Developing a Model for Person Estimation in Puerto Rico for the 2010 Census Coverage Measurement Program Colt S. Viehdorfer source 12
SESUG 2010 Fitting multivariate random-effects models using SAS PROC GLIMMIX Lei Li,
Namasivavayam Ambalavanan,
Abhik Das		 source 8
SESUG 2010 Potential Change in Reliability Measures Based on Decreased Sample Size for the Census Coverage Measurement Survey Vincent Thomas Mule source 12
SESUG 2010 SAS Macros for Estimating the Attributable Benefit of an Optimal Treatment Regime Jason Brinkley source 12
SESUG 2010 The Next Generation: SAS/STAT 9.22 Maura E. Stokes,
Bob Rodriguez,
Robert A. Cohen		 source 20
SGF 2010 An Analytical Approach to Determining Customer Value in the Property and Casualty Insurance Industry Roosevelt Mosley source 20
SGF 2010 An Introduction to Multiple Imputation of Complex Sample Data Using SAS 9.2 Patricia A. Berglund source 12
SGF 2010 Applications of PROC ESM in SAS 9.2 Anders Milhøj source 11
SGF 2010 Creating Forest Plots from Pre-computed Data Using PROC SGPLOT and Graph Template Language Zoran Bursac source 9
SGF 2010 Customizing ODS Statistical Graphs Step-by-Step Dongsheng Yang source 8
SGF 2010 Ideas and Examples in Generalized Linear Mixed Models David A. Dickey source 12
SGF 2010 Improving Your Statistical Consulting Prowess by Adding R to Your SAS Repertoire Mark Austen,
Jeffrey M. Gossett,
Maria S. Melguizo,
Chan-Hee Jo		 source 11
SGF 2010 Introducing PROC PLM and Postfitting Analyses in Very General Linear Models Randy Tobias,
Weijie Cai		 source 18
SGF 2010 SAS 9.2 Business Analytics Framework and IBM PowerVM Virtualization Deployment Scenarios Darrell Barton,
Frank Battaglia,
Alfredo Mendoza		 source 12
SGF 2010 The Next Generation: SAS/STAT 9.22 Maura E. Stokes,
Robert N. Rodriguez		 source 20
SGF 2010 Using Recycled Predictions for Computing Marginal Effects Zhongmin Li,
Geeta Mahendra		 source 10
SGF 2010 Using the Descriptor Portion of a SAS Data File Dragos D. Capan source 6
SGF 2010 Using the NLMIXED Procedure to Fit Linear and Nonlinear Growth Curve Models Mo Zheng source 6
SGF 2010 Using the SAS Survey Procedures for Subpopulation Analysis with Jackknife Repeated Replication Methods in SAS 9.2 Zhuoqiao Wang,
William R. Waldron		 source 9
SGF 2010 Using the SAS System and SAS Enterprise Miner for Data Mining: A Study of Cancer Survival at the Mayo Clinic Leonard Gordon source 6
SESUG 2009 A Logistic Regression Model to Predict Freshmen Enrollments Vijayalakshmi Sampath,
Andrew Flagel		 source 12
SESUG 2009 A New Effect Modification P Value Test Demonstrated Manojkumar B Agravat source 14
SESUG 2009 Customer Retention Predictive Modeling in HealthCare Insurance Industry Jin Su,
Kimberly Cooper,
Tina Robinson,
Brad Jordan		 source 8
SESUG 2009 Developing Business Failure Prediction Models Using SAS Software Oki Kim source 10
SESUG 2009 Short, Sweet and Simple ... how to do more with less in SAS Brian Spruell source 6
SESUG 2009 Using ODS Select to Limit SAS Output Matt Reich source 4
MWSUG 2009 A Class of Predictive Models for Multi-Level Risks Wensui Liu,
Chuck Vu,
Sandeep Kharidhi		 13
MWSUG 2009 A Comparison of Decision Tree and Logistic Regression Model Xianzhe Chen 10
MWSUG 2009 A SAS Macro to Compute Added Predictive Ability of New Markers in Logistic Regression Kevin F. Kennedy,
Michael J. Pencina		 9
MWSUG 2009 Analysis of Breast Cancer and Surgery as Treatment Options Beatrice Ugiliweneza 17
MWSUG 2009 Analysis of Metabolic Disorder - Gout Sireesha Ramoju 30
MWSUG 2009 Dietary Glycemic Load and Risk of Colon Cancer: A Guide to Data Management Svetlana Zelenskiy,
Lauren Byrne,
Cheryl L. Thompson,
Thomas C. Tucker,
David Bruckman,
Li Li		 16
MWSUG 2009 DO's and DON'Ts of Generating Performance Metrics Michael C. Frick 7
MWSUG 2009 Predictive Modeling in Enterprise Miner Versus Regression Patricia B. Cerrito 27
MWSUG 2009 Ranking Predictors in Logistic Regression Douglas Thompson 13
MWSUG 2009 Using graph template language to customize ODS statistical graphs Dongsheng Yang,
Anne S. Tang		 8
PNWSUG 2009 Wait Wait, Don't Tell Me… You're Using the Wrong Proc! David L. Cassell 11
NESUG 2009 Using the New SURVEY Procedures From a Modeling Perspective Jonas V. Bilenas 13
NESUG 2009 What Drives Physicians to Use A Particular Product when Prescribing Marc States 5
WUSS 2009 Automating the Process of Scoring a Generalized Linear Model Fitted using PROC GENMOD Prakash Gurumurthy source 9
WUSS 2009 Developing Business Failure Prediction Models Using SAS Software Oki Kim source 10
WUSS 2009 Using PROC LOGISTIC to Estimate Willingness to Pay for Fresh Produce Mikael Peterson source 4
WUSS 2009 Using Recycled Predictions for Estimating Marginal Effects Geeta Mahendra,
Zhongmin Li		 source 9
PharmaSUG 2009 Generating Model Based Subgroup Analysis Using SAS Procedures Tracy Lin,
Jie Huang		 4
PharmaSUG 2009 Illustrative Logistic Regression Examples using PROC LOGISTIC: New Features in SAS/STAT 9.2 Robert G. Downer,
Patrick J. Richardson		 11
PharmaSUG 2009 Image is Almost Everything: Displaying Statistics Via ODS R. Scott Leslie 8
PharmaSUG 2009 ROC analysis for the evaluation of continuous biomarkers: Existing tools and new features in SAS 9.2 Sanghyuk Shin 15
PharmaSUG 2009 The Existence of Maximum Likelihood Estimates for the Logistic Regression Model William F. McCarthy,
Nan Guo		 12
SGF 2009 A Class of Predictive Models for Multi-Level Risks Wensui Liu,
Chuck Vu,
Sandeep Kharidhi		 source 13
SGF 2009 A SAS Macro for Adaptive Regression Modeling George J. Knafl source 9
SGF 2009 Bayesian Modeling Using the MCMC Procedure Fang K. Chen source 22
SGF 2009 Beyond SAS/Genetics Amina Barhdadi,
Yassamin Feroz Zada,
Marie-Pierre Dube		 source 10
SGF 2009 Clinical SAS Programming: Analysis and Mitigation of Risk Parag Shiralkar source 6
SGF 2009 Generalizations of Generalized Additive Model (GAM): A Case of Credit Risk Modeling Wensui Liu,
Jimmy Cela,
Chuck Vu		 source 14
SGF 2009 Image is Almost Everything: Displaying Statistics Via ODS R. Scott Leslie source 8
SGF 2009 Min Logistikk: Using SAS BI Web Services in a Service-Oriented Architecture at Norway Post Delivering Information Securely to Customers on the Extranet Truls Andersen,
Frode Langseth,
Shiraz A. Bhaiji,
Stein Nyfløt		 source 22
SGF 2009 The Problem of Regression Assumptions and the Use of Predictive Modeling Patricia B. Cerrito source 20
WUSS 2008 Automating the process of choosing among highly correlated covariates for multivariable logistic regression Michael C. Doherty source 7
WUSS 2008 Demystifying Popular SAS Procedures. Lisa Smith source 17
WUSS 2008 Image is Almost Everything: Displaying Statistics Via ODS R. Scott Leslie source 5
WUSS 2008 Using Propensity Scores To Adjust For Treatment Selection Bias R. Scott Leslie source 7
SESUG 2008 Lithium Battery Analysis: Probability of Failure Assessment Using Logistic Regression Travis A. Moebes source 9
SESUG 2008 Reject Inference Methodologies in Credit Risk Modeling Derek Montrichard source 10
PHUSE EU 2008 A Collection of SAS Macros to Calculate Odds Ratios using Spline Regression Martin Gregory source 9
PHUSE EU 2008 Statistically guided review of safety data in clinical trials Michael O'Connell,
Harry Southworth		 source 12
MWSUG 2008 Classification of Breast Cancer Cells Using JMP Marie Gaudard 34
MWSUG 2008 Long-Term Value Modeling in the Automobile Industry Jeff Ames,
Cathy Hackett,
Bruce S. Lund		 6
MWSUG 2008 The Difference Between Predictive Modeling and Regression Patricia B. Cerrito 19
PNWSUG 2008 Wait Wait, Don't Tell Me… You're Using the Wrong Proc! David L. Cassell 11
NESUG 2008 Breakthrough Improvements through Reliable Modern Testing using SAS and JMP Sunil Kumar Gupta 14
NESUG 2008 Fitting Generalized Additive Models with the GAM Procedure in SAS 9.2 Weijie Cai 14
NESUG 2008 Proc Logistic Plus: The Power Of Variable Transformations Krishna Mehta,
Nitin K. Jain,
Varun Aggarwal		 4
NESUG 2008 PROC LOGISTIC: Traps for the unwary Peter L. Flom 8
PharmaSUG 2008 An Identification of Diagnostic and Procedure Codes Related to Death and Malnutrition, but not Length of Stay or Total Charges Salina Burns 15
PharmaSUG 2008 Automated SAS Macro for Adverse Event Logistic Regression Analysis Suhas R. Sanjee 8
PharmaSUG 2008 Automating the process of choosing among highly correlated covariates for multivariable logistic regression Michael C. Doherty,
Xiaochun Zhang		 8
PharmaSUG 2008 Joint Repair: A Statistical Analysis of Patient Data Grant Johnson 20
PharmaSUG 2008 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 14
PharmaSUG 2008 The Use of Propensity Scores and Instrumental Variable Methods to Adjust For Treatment Selection Bias R. Scott Leslie 8
PharmaSUG 2008 Using SAS Enterprise Guide 4.0 to find a Correlation between Mortality and Co-morbidities for Prostate Disease Patients Jeong W. Park 18
SGF 2008 Adventures in ODS: Producing Customized Reports Using Output from Multiple SAS Procedures Stuart D. Long,
Jeffrey M. Abolafia,
Lawrence Park		 source 23
SGF 2008 Analysis of Breast Cancer Using SAS Beatrice Ugiliweneza source 8
SGF 2008 Convergence Failures in Logistic Regression Paul D. Allison source 11
SGF 2008 Employing SAS Text Miner Methodology to Become a Customer Genius in Customer Churn Prediction and Complaint E-mail Management Kristof Coussement source 12
SGF 2008 Fitting Generalized Additive Models with the GAM Procedure in SAS 9.2 Weijie Cai source 14
SGF 2008 How to Use SAS to Fit Multiple Logistic Regression Models Anpalaki J. Ragavan source 13
SGF 2008 In Search for a Golden Algorithm Tamara S. Slipchenko,
Candice Bowman,
Catherine Sugar,
Allen L. Gifford		 source 9
SGF 2008 Lithium Battery Analysis: Probability of Failure Assessment Using Logistic Regression Travis A. Moebes source 9
SGF 2008 ODS and Output Data Sets: What You Need to Know Myra A. Oltsik source 13
SGF 2008 Old Versus New: A Comparison of PROC LOGISTIC and PROC GLIMMIX Rebecca Christofferson source 6
SGF 2008 Operational Risk: Economic Capital (EC) Modeling with a Loss Distribution Approach Sabri G. Uner source 14
SGF 2008 Price and Revenue Optimization for Banking Keith Renison,
Lorry Hardt Jr.		 source 7
SGF 2008 PROC MIXED: Underlying Ideas with Examples David A. Dickey source 18
SGF 2008 Reliability and Validity Testing using SAS/STAT Andrea Roofe source 8
SGF 2008 Steps to Success with the SAS Output Delivery System Andrew H. Karp source 15
SGF 2008 The Use of Propensity Scores and Instrumental Variable Methods to Adjust For Treatment Selection Bias R. Scott Leslie,
Hassan Ghomrawi		 source 8
SGF 2008 Updates to SAS Power and Sample Size Software in SAS 9.2 Wayne E. Watson source 10
SGF 2008 Using SAS Enterprise Miner to Prescribe a Pre-Screen Mailing Rex Pruitt source 6
SGF 2008 Using the SAS DATA Step and PROC SQL to Create Macro Arrays Stuart D. Long,
Edward Heaton		 source 11
NESUG 2007 An Introduction to SAS Stat Studio: A Programmable Successor to SAS/INSIGHT Rick Wicklin,
Peter Rowe		 18
NESUG 2007 Butting Heads on Matched Cohort Analysis Using SAS Software Melvin T. Alexander 12
NESUG 2007 Common Pitfalls in SAS Statistical Analysis Macros in a Mass Production Environment Huei-Ling Chen,
Aiming Yang		 9
NESUG 2007 Customizing Output for Regression Analyses Using ODS and the Data Step Zhenyi Xue 11
NESUG 2007 Implementing Multiple Imputation in an Automatic Variable Selection Scenario Krishna Mehta,
Mayank Rustagi,
Saurabh Kohli,
Siddharth Tiwari		 4
NESUG 2007 PROC LOGISTIC: The Logistics Behind Interpreting Categorical Variable Effects Taylor Lewis 7
NESUG 2007 Selecting the Appropriate Outlier Detection Technique for Common Industry Applications Kunal Tiwari,
Krishna Mehta,
Nitin Jain,
Ramandeep Tiwari,
Gaurav Kanda		 5
NESUG 2007 Summarizing the Power of Your Predictive Model with Receiver Operating Characteristic Curves Mithat Gonen 18
NESUG 2007 Using Macro and ODS to Overcome Limitations of SAS Procedures Wei (Lisa) Lin,
Jing Su		 7
NESUG 2007 Using SAS ODS to Extract and Merge Statistics from Multiple SAS Procedures into a Single Summary Report, a Detailed Methodology Stuart D. Long,
Jeffrey M. Abolafia,
Lawrence Park		 22
NESUG 2007 Using the New SURVEY Procedures From a Modeling Perspective Jonas V. Bilenas 13
NESUG 2007 Using the SAS DATA Step and PROC SQL to Create Macro Arrays Stuart D. Long,
Edward Heaton		 11
SESUG 2007 Data Mining Episode Groupers Patricia B. Cerrito source 13
SESUG 2007 PROC MIXED: Underlying Ideas with Examples David A. Dickey source 19
SESUG 2007 Virtual and Traditional Teams: Are they Really Different? (Development and Validation of a Measure using SAS/STAT) Andrea J Roofe source 12
WUSS 2007 An Example of SAS Application in Public Health Research --- Predicting Smoking Behavior in Changqiao District, Shanghai, China Ding Ding source 8
WUSS 2007 An Introduction to SAS Stat Studio: A Programmable Successor to SAS/INSIGHT Rick Wicklin,
Peter Rowe		 source 18
WUSS 2007 Automated Programming Techniques for the Data Warehouse Manager Renato G. Villacorte source 6
WUSS 2007 Automating Regression Analysis and PDF Report Output with the Use of Macros: Tricks for Disabling Default Delimiters and Making Use of Counters Svetlana Smorodinsky,
Bob McLaughlin		 source 7
WUSS 2007 Case Study: Development of an HIV casefinding algorithm with SAS Enterprise Miner Tamara Slipchenko,
Candice Bowman,
Yuexin (Cindy) Chen,
Catherine Sugar,
Allen L. Gifford		 source 9
WUSS 2007 Data Mining Using SAS Enterprise Miner Randall Matignon source 10
WUSS 2007 Recreating the SELECTION=SCORE Model Specification with the BEST=n Effect Selection Option for PROC SURVEYLOGISTIC Sara R. Adams,
David R. Mink,
Dave P. Miller		 source 8
WUSS 2007 The Use of Propensity Scores and Instrumental Variable Methods to Adjust For Treatment Selection Bias R. Scott Leslie source 8
WUSS 2007 Wait Wait, Don't Tell Me… You're Using the Wrong Proc! David L. Cassell source 11
SCSUG 2007 A SAS Macro for Hosmer and Lemeshow's Purposeful Selection Model Building Algorithm: Description and Performance David Williams,
Zoran Bursac,
David K. Hosmer		 6
SCSUG 2007 An Introduction to SAS Stat Studio: A Programmable Successor to SAS/INSIGHT Rick Wicklin,
Peter Rowe		 18
PharmaSUG 2007 A Review of Propensity Score Application in Healthcare Outcome and Epidemiology Guiping Yang 24
PharmaSUG 2007 Cochran-Armitage Trend Test Using SAS Hui Liu 11
PharmaSUG 2007 Effects of Multicollinearity in All Possible Mixed Model Selection George C. J. Fernandez 18
PharmaSUG 2007 Graphing Estimates of Regression Models for Discrete and Limited Dependent Variables Jianming He,
David Budd		 10
PharmaSUG 2007 SAS Markov Chain Monte Carlo (MCMC) Simulation in Practice Shi-Tao Yeh,
Scott D Patterson		 20
PharmaSUG 2007 Several Graph Types in Clinical Studies Long Zheng,
Suriani Pokta,
Pan-Yu Lai		 8
SGF 2007 A Peek at PROC DOCUMENT Andrew H. Karp source 5
SGF 2007 A Purposeful Selection of Variables Macro for Logistic Regression Zoran Gauss,
C. Heath Williams,
David K. Hosmer,
David W. Bursac		 source 5
SGF 2007 Alternatives for Analysis of Complex Sample Surveys: A Comparison of SAS, SUDAAN, and AM Software Bethany A. Kromrey,
Jeffrey D. Bell-Ellison		 source 10
SGF 2007 An Introduction to SAS Stat Studio: A Programmable Successor to SAS/INSIGHT Rick Wicklin,
Peter Rowe		 source 18
SGF 2007 An Introduction to SAS Stat Studio: A Programmable Successor to SAS/INSIGHT Rick Wicklin,
Peter Rowe		 source 18
SGF 2007 Improving Credit Scoring by Generalized Additive Model Wensui Cela,
Jimmy Liu		 source 9
SGF 2007 Introducing a SAS Macro for Doubly Robust Estimation Michele Westreich,
Daniel Davidian,
Marie Wiesen,
Chris Jonsson Funk		 source 8
SGF 2007 Keynote Presentation: Evolving Wholesale Energy Markets Demand New Technology Solutions Patrick Reames source 14
SGF 2007 Marginal Interpretation of Subject-Specific Curves: Logistic-Normal Regression Pedro A. Macchiavelli,
Raul Torres		 source 10
SGF 2007 Parameter Gathering Techniques for Data Driven Programming Renato G. Villacorte source 5
SGF 2007 Predicting Software Maintenance Outcomes Using Data Mining and Text Mining Uzma Raja source 9
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
SGF 2007 Saving Trees with Output Delivery System (ODS) Angelina D. Diehl,
Nancy Mandrekar,
Jayawant N. Tan		 source 9
SGF 2007 Text Mining and PROC KDE to Rank Nominal Data Patricia B. Cerrito source 21
SGF 2007 Using Propensity Scores To Adjust For Treatment Selection Bias R. Scott Thiebaud,
Patrick Leslie		 source 4
SGF 2007 Variable Screening for Multinomial Logistic Regression on Very Large Data Sets as Applied to Direct Response Modeling John A. Cherrie source 12
PNWSUG 2006 Wait Wait, Don't Tell Me… You're Using the Wrong Proc! David L. Cassell 11
PNWSUG 2006 You Can't Stop Statistics: SAS/STAT Software Keeps Rolling Along Maura E. Stokes,
Robert N. Rodriguez		 32
SESUG 2006 Using SAS ODS to Extract and Merge Statistics from Multiple SAS Procedures into a Single Summary Report, a Detailed Methodology Stuart D. Long source 21
WUSS 2006 Find the Best Prospects for a New Product by Using a Data Mining Model Ting Wu source 8
WUSS 2006 Hierarchical Logistic Regression Modeling with SAS GLIMMIX Jian Dai,
Zhongmin Li,
David Rocke		 source 9
WUSS 2006 Parameter Gathering Techniques for Data Driven Programming Renato G. Villacorte source 5
WUSS 2006 PROC GENMOD with GEE to Analyze Correlated Outcomes Data Using SAS Tyler C. Smith,
Besa Smith		 source 14
WUSS 2006 Sample Survey Data and the Procs you OUGHT To Be Using David L. Cassell source 13
WUSS 2006 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 source 14
WUSS 2006 Using Propensity Scores To Adjust For Treatment Selection Bias Scott P. Leslie,
Patrick Thiebaud		 source 4
WUSS 2006 Wait Wait, Don't Tell Me… You're Using the Wrong Proc! David L. Cassell source 11
NESUG 2006 Logistic Regression Basics Joseph J. Guido,
Paul Winters,
Adam Rains		 7
NESUG 2006 Matching with Propensity Scores to Reduce Bias in Observational Studies Marcelo Coca-Perraillon,
Nan Liu		 10
NESUG 2006 Receiver Operating Characteristic (ROC) Curves Mithat Gonen 18
NESUG 2006 Segmentation For Insurance Payments Michael Sherlock 10
NESUG 2006 Statistical analysis of clustered data using SAS Gui-Shuang Ying,
Chengcheng Liu		 13
NESUG 2006 Survey Logistic Regression: Some SAS-SUDAAN Comparisons Xiuhua Chen 7
NESUG 2006 The Philadelphia Story (Or the LUG at the RUG) John J. Cohen,
Robert Schechter		 10
NESUG 2006 Using SAS to Analyze the Summary Data Zhenyi Xue 6
NESUG 2006 You Can't Stop Statistics Maura E. Stokes,
Robert N. Rodriguez		 32
PharmaSUG 2006 Analyzing Ordinal Repeated Measures Data Using SAS Bin Yang 8
PharmaSUG 2006 From GLM to GLIMMIX-Which Model to Choose? Patricia B. Cerrito 7
SUGI 2006 Analysis of Large Hierarchical Data with Multilevel Logistic Modeling Using PROC GLIMMIX Jia Li,
Toni Alterman,
James A. Deddens		 source 5
SUGI 2006 Application of Experimental Design in Consumer Direct-Mail Solicitations Jonas V. Bilenas source 9
SUGI 2006 Application of Proc Discrim and Proc Logistic in Credit Risk Modeling Jin Li source 9
SUGI 2006 Assessing Within- and Between-Individual Effects Using Correlated Data from an HIV Clinical Trial Marla J. Husnik,
Grant Colfax,
Eugene Huang		 source 10
SUGI 2006 Biosurveillance and Outbreak Detection Using the ARIMA and LOGISTIC Procedures Ernest S. Shtatland source 8
SUGI 2006 Combining the Power of ODS, Data Set Concatenation, and DDE to Output Customized Statistical Results from SAS to Microsoft Excel Navneet K. Dhand,
Jenny-Ann Toribio		 source 10
SUGI 2006 Exploring Alternative Predictive Modeling Techniques to Strengthen the Customer Relationship David Bordeleau source 5
SUGI 2006 Fixed Effects Regression Methods in SAS Paul D. Allison source 20
SUGI 2006 Receiver Operating Characteristic (ROC) Curves Mithat Gonen source 18
SUGI 2006 The Big Four: Analyzing Complex Sample Survey Data Using SAS, SPSS, STATA, and SUDAAN Arlene B. Siller,
Linda Tompkins		 source 5
SUGI 2006 The Enigma of Survey Data Analysis: Comparison of SAS Survey Procedures and SUDAAN Procedures Katherine L. Baisden,
Paul Hu		 source 11
SUGI 2006 U.S. Health and Nutrition: SAS Survey Procedures and NHANES Jeffrey M. Gossett,
Chan-Hee Jo,
Pippa M. Simpson		 source 9
SUGI 2006 Using SAS ODS to Extract and Merge Statistics from Multiple SAS Procedures Stuart D. Long,
Jeffrey M. Abolafia,
Lawrence Park		 source 21
SUGI 2006 Wait Wait, Don't Tell Me… You're Using the Wrong Proc! David L. Cassell source 11
SUGI 2006 You Can't Stop Statistics Maura E. Stokes,
Robert N. Rodriguez		 source 32
PNWSUG 2005 A Peek at PROC DOCUMENT Andrew H. Karp 25
PNWSUG 2005 Assessing Within- and Between-Subject Effects with Correlated Clinical Data Marla J. Husnik,
Eugene Huang,
Grant Colfax		 10
SESUG 2005 An Introduction to ODS for Statistical Graphics in SAS 9.1 Robert N. Rodriguez source 27
SESUG 2005 Using ODS to Perform Simulations on Statistics from SAS Procedures Andrew A. Kramer source 4
MWSUG 2005 Comparison Of Enterprise Miner And SAS/STAT For Data Mining Patricia B. Cerrito 12
WUSS 2005 A Peek at PROC DOCUMENT Andrew H. Karp source 5
WUSS 2005 Analysis of Group-Randomized Designs: A clinic-based breast cancer rescreening example Jesse Albert Canchola,
Regina Otero-Sabogal		 source 9
WUSS 2005 Combining Segmentation and Targeting Modeling: A Winning Formula Aaron Lai,
Howell Kiung		 source 3
NESUG 2005 Introduction to Experimental Design using SAS Jonas V. Bilenas 13
NESUG 2005 Logistic Regression: Ordinal and Nominal Outcomes Peter L. Flom 9
NESUG 2005 Predictive Modeling Using Survival Analysis Vadim Pliner 10
NESUG 2005 The Big Four: Analyzing Complex Sample Survey Data Using SAS, SPSS, STATA, and SUDAAN Arlene B. Siller,
Linda Tompkins		 7
PharmaSUG 2005 An Introduction to ODS for Statistical Graphics in SAS 9.1 Robert N. Rodriguez 27
PharmaSUG 2005 Analyze Group-randomized Trial Data with Binary Outcome Using Weighted Paired t-test: A SAS Macro Don (Dongguang) Li 5
PharmaSUG 2005 Assessing Within- and Between-Subject Effects with Correlated Clinical Data Marla J. Husnik,
Eugene Huang,
Grant Colfax		 10
PharmaSUG 2005 Using Procedure-Based ODS Data Components in Statistical Reporting Vincent J. Faber 10
SUGI 2005 A Proportional Hazards Approach to Campaign List Selection Charles Manahan source 7
SUGI 2005 Analyzing Incomplete Binary Repeated Measures Data Using SAS Bin Yang source 8
SUGI 2005 Combining Decision Trees with Regression in Predictive Modeling with SAS Enterprise Miner Kattamuri S. Sarma source 10
SUGI 2005 Imputation of Categorical Variables with PROC MI Paul D. Allison source 14
SUGI 2005 IRT-FIT: SAS Macros for Fitting Item Response Theory (IRT) Models Robert Terry,
Sung-Hyuck Lee		 source 9
SUGI 2005 Model Building in PROC PHREG with Automatic Variable Selection and Information Criteria Emily M. Cain,
Ken Kleinman,
Ernest S. Shtatland		 source 10
SUGI 2005 Parameterizing Models to Test the Hypotheses You Want: Coding Indicator Variables and Modified Continuous Variables David J. Pasta source 20
SUGI 2005 Power Simulation for Categorical Data Using the RANTBL Function Joel A. Dubin,
Theodore R. Holford,
Peter H. Van Ness		 source 12
SUGI 2005 Predicting Customer Value Will Potts source 6
SUGI 2005 Structured Neural Network Techniques for Modeling Loyalty and Profitability Carl Lee,
Tim Rey,
James W. Mentele,
Michael Garver		 source 13
SUGI 2005 Teaching Data Mining in a University Environment David A. Dickey source 11
SUGI 2005 Using Procedure-Based ODS Data Components in Statistical Reporting Vincent J. Faber source 10
SUGI 2005 Using SAS to Estimate Proportional Odds Ratio Model for Meta-Analysis of Diagnostic Tests Mir S. Siadaty,
Jianfen Shu		 source 10
SUGI 2005 Using the Proportional Odds Model for Health-Related Outcomes: Why, When, and How with Various SAS Procedures Marc Gameroff source 8
NESUG 2004 An Introduction to ODS for Statistical Graphics in SAS 9.1 Maura E. Stokes 27
NESUG 2004 Building Logistic Regression Models to Analyze Survey Data with Complex Sampling Design Zhenyi Xue,
Euni Lee,
Monika Datary,
Anthony Wutoh		 4
NESUG 2004 Thumbs Up For ODS Graphics, But Don't Throw Out All Your SAS/GRAPH Programs! Richard M. Mitchell 16
SESUG 2004 Binary Logistic Regression Model Optimization Jerry Musial source 6
WUSS 2004 A Peek at PROC DOCUMENT Andrew H. Karp source 5
WUSS 2004 An Approach to Record-Linkage Using Propensity Score Paul D. Frederick source 12
WUSS 2004 An Introduction to ODS for Statistical Graphics in SAS 9.1 Robert N. Rodriguez source 27
WUSS 2004 Beyond Binary Outcomes: PROC LOGISTIC to Model Ordinal and Nominal Dependent Variables Eric P. Elkin source 8
WUSS 2004 Package, Archive, Report and Test Statistical Models Using ODS Ted Clay source 10
WUSS 2004 Parameterizing Models to Test the Hypotheses You Want: Coding Indicator Variables and Modified Continuous Variables David J. Pasta source 12
WUSS 2004 PROC GENMOD for Analysis of Correlated Outcome Data Using the LOGIT Link Function Tyler C. Smith,
Besa Smith,
William Honner,
Ming Ji		 source 14
WUSS 2004 The Continuing Quandary of Survey Data Part II:Comparison of SAS Procedures and SUDAAN Procedures Katherine L. Baisden source 20
WUSS 2004 Using Classification Tree Outcomes to Enhance Logistic Regression Models Tim Millard source 4
MWSUG 2004 Analysis of Complex Sample Survey Data Patricia A. Berglund 7
MWSUG 2004 Evaluating Continuous Variable Transformations in Logistic Regression Maggie (Yu) Zhang,
Shaolin Chen,
Suzanne C. Rain		 12
MWSUG 2004 Presenting the Binary Logit Model Effectively using SAS Hun Myoung Park 11
PNWSUG 2004 A Peek at PROC DOCUMENT Andrew H. Karp 25
SUGI 2004 A New Strategy of Model Building in SAS PROC LOGISTIC with Automatic Variable Selection, Validation, Shrinkage, and Model Averaging Ernest S. Shtatland,
Ken Kleinman,
Emily M. Cain		 source 11
SUGI 2004 An Approach to Record-Linkage Using Propensity Score Paul D. Frederick source 13
SUGI 2004 An Introduction to Matching and Its Application Using the SAS System Jayawant N. Mandrekar,
Sumithra J. Mandrekar		 source 9
SUGI 2004 An Introduction to ODS for Statistical Graphics in SAS 9.1 Robert N. Rodriguez source 28
SUGI 2004 Fitting Longitudinal Mixed Effect Logistic Regression Models with the NLMIXED Procedure Peter H. Van Ness,
John R. O'Leary,
Amy L. Byers,
Terri R. Fried,
Joel A. Dubin		 source 7
SUGI 2004 Head of the CLASS: Impress Your Colleagues with a Superior Understanding of the CLASS Statement in PROC LOGISTIC Michelle L. Pritchard,
David J. Pasta		 source 9
SUGI 2004 Message Queue-Based Scoring Interface David R. Duling,
Robert Chu		 source 6
SUGI 2004 Performing a 1:N Case-Control Match on Propensity Score Lori S. Parsons 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
SUGI 2004 Thumbs Up For ODS Graphics, But Don't Throw Out All Your SAS/GRAPH Programs! Richard M. Mitchell source 17
WUSS 2003 A Peek at PROC DOCUMENT Andrew H. Karp source 4
WUSS 2003 A SAS Macro Solution for Scoring Test Data with Output Features and Parameters from Logistic Regression Model Training Joseph Blue source 5
WUSS 2003 Adaptive Regression in SAS/IML David Katz source 5
WUSS 2003 Advanced Analytics with Enterprise Guide Catherine Truxillo,
Stephen McDaniel		 source 9
WUSS 2003 Answering Your Questions with Statistics Diana D. Suhr source 6
WUSS 2003 Getting Started with PROC LOGISTIC Andrew H. Karp source 6
WUSS 2003 Logistic regression model-building strategies for predicting regular mammography screening adherence among uninsured immigrant women Jesse Albert Canchola,
Regina Otero-Sabogal,
Desiree Owens,
Farzaneh Tabnak		 source 6
WUSS 2003 Presenting Logistic Regression Models to Non-Statisticians: Adjusted Probabilities and Adjusted Risk Ratios David J. Pasta,
Miriam G. Cisternas		 source 4
WUSS 2003 Using SAS for Classical Item Analysis and Option Analysis Chong Ho Yu,
Josephine Wai-chi Wong		 source 6
WUSS 2003 Using SAS IN Educational Research Leping Liu source 6
SESUG 2003 Advantages of PROC SCORE Mark Tabladillo source 2
SESUG 2003 Management of CABG Patient Mortality Using SAS Logistic Regression by Cohorts: Age, Sex and Cleveland Clinic defined risk factors Five years of data and analysis compressed onto a 3 X 5 pocket guide Lucinda Jenkins,
Bing Li,
Mike Mueller,
Steve Hadzima		 source 7
NESUG 2003 Backwards Manual Selection (%bms): A Macro for Selecting Logistic Regression Models to Address Epidemiological Research Questions Janice Hegewald,
Anette Pfahlberg,
Wolfgang Uter		 5
SEUGI 2003 New Java components that exploit the SAS OLAP Server 9.1 [presentation] Ingo Brenckmann source 20
SEUGI 2003 Nokia Mobile Phones - Performance Management in global operations with SAS ABM Solutions [presentation] Mika Ojala source 9
SEUGI 2003 SAS Supply Chain Intelligence - Or how to manage the Flood [presentation] Gerhard Held source 37
VIEWS 2003 Customer churn - An operational comparison of logistic regression, decision tree & neural network models. Arun Parekkat 4
PharmaSUG 2003 Likelihood Approach in Estimation and Testing Problems Involving Grouped Data Daniel Bonzo,
InfoVision Consultants		 8
PharmaSUG 2003 Performance Tuning of Java-based Application Development for Clinical Pharmacogenomics Associations Zongwei Shen,
Richard Judson,
Robert Tracy,
Theodore Kalbfleisch		 4
PharmaSUG 2003 Selecting Balanced Cohorts for Comparison in Observational Research Craig Roberts 4
PharmaSUG 2003 Using SAS Software and Visual Basic for Applications to Produce Microsoft Graph Charts Lori S. Parsons 10
SUGI 2003 Fast and Easy Ways to Annoy a SAS Programmer: A Statistician's Revenge! Richard M. Mitchell source 10
SUGI 2003 Logistic Regression Modeling - JMP StartTM Your Analysis with a Tree Pippa M. Simpson,
Renée A. Hall,
James G. Parker,
Jeffrey M. Gossett		 source 5
SUGI 2003 Multistage Cross-Sell Model of Employers in the Financial Industry Kwan Park,
Steve Donohue		 source 5
SUGI 2003 SAS Macros and Tools for Working with Weighted Logistic Regression Models That Use Survey Data David Izrael,
Annabella A. Battaglia,
David C. Hoaglin,
Michael P. Battaglia		 source 10
SUGI 2003 STEPWISE Methods in Using SAS PROC LOGISTIC and SAS Enterprise MinerTM for Prediction Ernest S. Shtatland,
Ken Kleinman,
Emily M. Cain		 source 6
SUGI 2003 The Use of Geographic Information Systems to Investigate Environmental Pollutants in Relationship to Medical Treatment Patricia B. Cerrito,
Robert Forbes,
George R. Barnes		 source 6
SUGI 2003 Using SAS Software and Visual Basic for Applications to Produce Microsoft Graph Charts Lori S. Parsons source 10
MWSUG 2002 A Macro to Assess the Key Drivers of Overall Product Acceptance using SAS System George J. Hurley,
David Hengehold		 7
SCSUG 2002 Statistical Pattere Recognition Using Enterprise Miner and Self-Organizing Maps Keith A. Jones 8
NESUG 2002 An Explicit Functional Form Specification Approach to Estimate the Area under a Receiver Operating Characteristic (ROC) Curve Paul Stober,
Shi-Tao Yeh		 5
NESUG 2002 One More Time About R2 Measures of Fit in Logistic Regression Ernest S. Shtatland,
Ken Kleinman,
Emily M. Cain		 6
NESUG 2002 Practice of SAS Logistic Regression on Binary Pharmacodynamic Data - Problems and Solutions Alan Xiao 5
NESUG 2002 Two Useful Macros to Nudge SAS to Serve You David Izrael,
Michael P. Battaglia		 6
NESUG 2002 User-Interface Tool Choice and Audit Trail Tool Choice for a SAS Based Data Entry/Verify System for Clinical Trials Data Barry R. Cohen 6
NESUG 2002 Using the LOGISTIC Procedure to Model Responses to Financial Services Direct Marketing David Marsh 6
SESUG 2002 Analysis of Dosage-Response Data in Agricultural Research K. Bondari source 6
SESUG 2002 Automated Summarization of Risk Assessment of Discrete and Continuous Exposures in Epidemiological Studies using SAS ODS Stuart D. Long source 6
SESUG 2002 Handling Missing Data with Multiple Imputation Using PROC MI in SAS Hossein N. Yarandi source 5
SESUG 2002 High Energy SAS - Managing the SAS Community in a Large Corporation James M. Alessio,
Mark L. Jordan		 source 5
SEUGI 2002 Adding Value Through Supply Chain Intelligence [presentation] Stephen Toms source 16
SEUGI 2002 Data Management and Data Analysis Solutions in Manufacturing: 10 Years Experience at Altis Semiconductor [presentation] J. L. Grolier source 36
SEUGI 2002 Improving Bank Profits with Next-Generation Data Mining Techniques [presentation] Rafael Alverez Cuesta source 17
SEUGI 2002 Innovations in Cash Logistics: CoBaCash - An intranet based system for optimizing cash supply for branches and ATMs [presentation] A. Ruban,
L. Nottekämper		 source 25
SEUGI 2002 Japanese Corporate Bankruptcy Prediction Model [presentation] Iwabuchi source 22
SEUGI 2002 Know whom your cusomers listen to - Scoring for marketing channels reception propensity [presentation] Pawel Gasiorowski,
Wojciech Latusek		 source 20
SEUGI 2002 New tools for a new context: Enterprise Miner in the French gas industry [presentation] Herve Augendre source 17
SEUGI 2002 SAS Meets Big Iron: High Performance Computing in SAS Analytic Procedures [presentation] Robert A. Cohen source 61
SEUGI 2002 Specialized Masters in Business Intelligence Jean-Francois Fiorina source 5
PharmaSUG 2002 Choice of Development Tool for the User Interface of a Client-Server Application in a SAS Environment Barry R. Cohen 6
PharmaSUG 2002 Macros for Model Building, Summarizing, and Displaying Results of Stepwise Logistic Regressions Lori S. Parsons 6
PharmaSUG 2002 Performing Logistic Regression on Survey Data with the New SURVEYLOGISTIC Procedure Anthony B. An 10
SUGI 2002 A Proportional Odds/Hazards Approach to Analyzing Likert Scale Data Lynette K. Duncan,
James E. Dunn		 source 5
SUGI 2002 An Explicit Functional Form Specification Approach to Estimate the Area under a Receiver Operating Characteristic (ROC) Curve Paul W. Stober,
Shi-Tao Yeh		 source 6
SUGI 2002 Analysis of Complex Sample Survey Data Using the SURVEYMEANS and SURVEYREG Procedures and Macro Coding Patricia A. Berglund source 5
SUGI 2002 Automating the Building of 2nd and 3rd Order Interactions and the Construction of Hierarchical Logistic Regression Models Mazen Abdellatif,
Robert G. Anderson,
Domenic J. Reda		 source 4
SUGI 2002 Communicating the Results of Predictive Models to Non-technical Audiences Andrew H. Karp source 4
SUGI 2002 Fast and Easy Ways to Annoy a SAS Programmer: A Statistician's Revenge! Richard M. Mitchell source 10
SUGI 2002 Fast and Easy Ways to Annoy a Statistician: The Sharing and Presentation of Data Between a SAS Programmer and a Statistician Richard M. Mitchell source 10
SUGI 2002 High Energy SAS - Managing the SAS Community in a Large Corporation Mark L. Jordan,
James M. Alessio		 source 5
SUGI 2002 How to Use SAS for Logistic Regression with Correlated Data Oliver Kuss source 5
SUGI 2002 Identifying Plant Species: A Botanical Analysis Using PROC DISCRIM Robert G. Downer,
Philip E. Hyatt		 source 4
SUGI 2002 Macros for Model Building, Summarizing, and Displaying Results of Stepwise Logistic Regressions Lori S. Parsons source 6
SUGI 2002 Performing Logistic Regression on Survey Data with the New SURVEYLOGISTIC Procedure Anthony B. An source 9
SUGI 2002 Redesigning Experiments with Polychotomous Logistic Regression: A Power Computation Application Charles M. Vaughan,
Serge Guzy		 source 6
SUGI 2002 SAS Tools for Meta-analysis Robert M. Hamer,
Pippa M. Simpson		 source 6
SUGI 2002 To Neural or Not to Neural? - This Is the Question Leon L. Fedenczuk source 6
SUGI 2002 Use of the ROC Curve and the Bootstrap in Comparing Weighted Logistic Regression Models David Izrael,
Annabella Battaglia,
David C. Hoaglin,
Michael P. Battaglia		 source 6
SUGI 2002 User-Interface Tool Choice and Audit Trail Tool Choice for a SAS Based Data Entry/Verify System for Clinical Trials Data Barry R. Cohen source 6
SUGI 2002 Using SAS Enterprise Miner for Data Quality Monitoring in the Veterans Health Administration's External Peer Review Program James H. Forsythe source 5
MWSUG 2001 Modeling Lifetime Value in the Insurance Industry C. Olivia Parr Rud 9
NESUG 2001 Determination and Interpretation of the OOP for ROC's with PROC LOGISTIC Robert J. Gallop 6
NESUG 2001 Fast and Easy Ways to Annoy a Statistician: The Sharing and Presentation of Data Between a SAS Programmer and a Statistician Richard M. Mitchell 10
NESUG 2001 Introduction to Enterprise Miner. Destiny Corporation (a company) 2
NESUG 2001 Replicate-Based Variance Estimation in a SAS Macro Julia L. Bienias 9
NESUG 2001 The Study of Different Context on NAEP Math Basket (Short Form): Implementation Using SAS/Base and SAS/Graph Brenda Siok-Hoon Tay-Lim 5
VIEWS 2001 Data Mining and Statistical Models in Marketing Campaigns of BT Retail Francesco Vivarelli,
Martyn Johnson		 9
VIEWS 2001 The Version 8 Applied Statistician David Shannon 15
WUSS 2001 Estimating and Interpreting Ordered Regression Models using PROC PRINT Kenneth Fernandez source 5
WUSS 2001 Using SAS to Assess Students' Learning in Instructional Technology Courses Leping Liu source 6
SESUG 2001 Are Strings Tying You in Knots? Deb Cassidy source 10
SESUG 2001 GETTING STARTED WITH PROC LOGISTIC Andrew H. Karp source 5
SESUG 2001 Using the SAS System to Demonstrate the equivalence of On-line and On-paper Survey Administration across Levels of Raters Jim Penny source 5
SESUG 2001 Using the SAS System to Study the Gender and Level Measurement Equivalence of a Multi-rater Survey Multi-rater Survey source 6
SCSUG 2001 Getting Started with PROC LOGISTIC Andrew H. Karp 5
SCSUG 2001 Ideas on Variable Selection and Alternative Links in Procedure CATMOD Kimberly DeJarnatt,
James E. Dunn		 4
SCSUG 2001 Using the SAS System to Demonstrate the Equivalence of On-line and On-Papeer Survey Adminstration across Levels of Raters Jim Penny 5
SCSUG 2001 Using the SAS System to Study the Gender and Level Measurement Equivalence of a Multi-rater Survey Jim Penny 6
SEUGI 2001 A retrospective evaluation of a data mining approach to fetal asphyxia at delivery in a hospital database [presentation] Olavi Kauhanen,
Seppo Heinonen		 source 53
SEUGI 2001 Detecting Procurement Fraud [presentation] Jennifer Major source 33
SEUGI 2001 Knowledge Solution for Credit Scoring [presentation] Henrik Wagner source 26
SEUGI 2001 The sweet side of web warehousing [presentation] Alan Buda,
Daniele Frisoni,
Leonardo Modeo		 source 21
SEUGI 2001 Wireless, hype or reality, can you really apply it to the business? [presentation] Stephan Haux source 20
PharmaSUG 2001 Fitting Generalized Additive Models with the GAM Procedure Dong Xiang 8
PharmaSUG 2001 Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques Lori S. Parsons 6
SUGI 2001 A Reader's Guide toVisualizing Categorical Data Michael Friendly source 8
SUGI 2001 A SAS/IML Macro for Goodness-of-Fit Testing in Logistic Regression Models With Sparse Data Oliver Kuss source 5
SUGI 2001 Fast and Easy Ways to Annoy a Statistician: The Sharing and Presentation of Data Between a SAS Programmer and a Statistician Richard M. Mitchell source 10
SUGI 2001 Fitting Generalized Additive Models with the GAM Procedure Dong Xiang source 8
SUGI 2001 Getting Started with PROC LOGISTIC Andrew H. Karp source 5
SUGI 2001 Model Fitting in PROC GENMOD Jean G. Orelien source 6
SUGI 2001 Modeling Lifetime Value in the Insurance Industry C. Olivia Parr Rud source 8
SUGI 2001 Partitioned Mahalanobis D^2 to Improve GIS Classification Lynette K. Duncan,
James E. Dunn		 source 6
SUGI 2001 Predictive Modeling in the Insurance Industry Using SAS Terry J. Woodfield source 5
SUGI 2001 Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques Lori S. Parsons source 6
SUGI 2001 The Perils of Stepwise Logistic Regression and How to Escape Them Using Information Criteria and the Output Delivery System Ernest S. Shtatland,
Mary B. Barton,
Emily M. Cain		 source 6
SUGI 2001 Variable Reduction for Modeling Using PROC VARCLUS Bryan D. Nelson source 3
SESUG 2000 Computation of pvalues for Multiple Comparisons with a Control in the SAS System Jean G. Orelien source 9
SESUG 2000 Computing the Approximate and Exact Leaving-One-Out Method in Multiple Group Logistic Discrimination with the SAS System Mohamed H. Hussein source 6
SESUG 2000 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 7
SESUG 2000 Performing Exact Logistic Regression with the SAS R /AD System Robert E. Derr source 10
SESUG 2000 Using Data Mining Technology to Identify and Prioritize Emerging Opportunities for Mortgage Lending to Homeownership-Deficient Communities Mortgage Lending source 10
NESUG 2000 Ad hoc Validation of Multivariate Regression Using PROC FACTOR with BY Groups Susan B. Garavaglia,
Asha Sharma,
Stephen Thompson		 10
NESUG 2000 Getting Started with PROC LOGISTIC Andrew H. Karp 5
NESUG 2000 How to Be a Bayesian in SAS: Model Selection Uncertainty in PROC LOGISTIC and PROC GENMOD Ernest S. Shtatland 9
NESUG 2000 Methods for the Analysis of Clustered Ordinal Response Data Robert J. Gallop,
J. Richard Landis,
Thomas R. Ten Have		 7
NESUG 2000 NPV Modeling for Direct Mail Insurance C. Olivia Parr Rud 7
NESUG 2000 Ordinal Outcomes with the Continuation Ratio Model Melissa J. McGowan 4
NESUG 2000 Risk-Adjustment of Cesarean Section Rates as Part of Clinical Quality Improvement: Two Applications of PROC LOGISTIC Guy Pascale 3
WUSS 2000 A Model of Integrating Technology into Classrooms: A Logistic Regression Analysis Leping Liu source 6
WUSS 2000 Analyzing Multiple Health Outcomes Using the SAS System's PROC CATMOD Besa Smith source 5
WUSS 2000 Data Warehousing for Data Mining: A Case Study C. Olivia Parr Rud source 9
WUSS 2000 Getting Started with PROC LOGISTIC Andrew H. Karp source 5
WUSS 2000 SAS STAT applications in Econometrics Mathura Thillainathan,
George C. J. Fernandez		 source 10
SEUGI 2000 An ordered list of customers for a new Bank: scoring with EM 3.0 [presentation] Flavio Bonifacio source 14
SEUGI 2000 Analyzing accounting information on the Internet [presentation] J. L. Machal,
K. Rogiers,
M. Bourgois		 source 19
SEUGI 2000 Cost-Cotrolling via e-mail using Enterprise Reporter on top of SAP/R3 [presentation] Paul Bruynseels source 38
SEUGI 2000 Credit Risk Measurement for a Bank Loans Portfolio Emiliano Laruccia,
Valter Lazzari		 source 11
SEUGI 2000 Data Mining in a Pharmaceutical Environment [presentation] Franky De Cooman source 24
SEUGI 2000 From the Reporting System to the Web Warehousing: How the BI/DW technology is increasing its importance in a Small/Medium company. The Worldgem Group successfully experience. [presentation] Andrea Provini,
Gianni Lorato		 source 4
SEUGI 2000 SAS can do it - now what did you want and how did you want it? Alyson Cromar,
Richard Coleman		 source 6
SEUGI 2000 SAS Software for the Analysis of Epidemiologic Data [presentation] Hans-Peter Altenburg source 39
SEUGI 2000 The Art of Building Predictive Scorecards [presentation] Duncan Hale source 15
PharmaSUG 2000 An Alternative to the Odds Ratio: A Method for Comparing Adjusted Treatment Group Effects on a Dichotomous Outcome P. Chris Holland 5
SUGI 2000 Data Warehousing for Data Mining: A Case Study C. Olivia Parr Rud source 9
SUGI 2000 Getting Started with PROC LOGISTIC Andrew H. Karp source 5
SUGI 2000 Performing Exact Logistic Regression with the SAS System Bob Derr source 10
SUGI 2000 Using Propensity Scores to Adjust for Group Differences: Examples Comparing Alternative Surgical Methods David J. Pasta source 7
SUGI 2000 Using SAS Software to Perform a Case-Control Match on Propensity Score in an Observational Study Lori S. Parsons source 6
SUGI 2000 Visualizing Categorical Data: Data, Stories, and Pictures Michael Friendly source 9
SUGI 2000 Why We Need an R-Square Measure of Fit (and Not Only One) In PROC LOGISTIC and PROC GENMOD Ernest S. Shtatland,
Sara L. Moore,
Mary B. Barton		 source 6
SESUG 1999 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 7
SESUG 1999 Forecasting Army Officer Attrition with Logistic Regression Using The SAS System Douglas L. McAllaster source 10
SESUG 1999 Using the SAS System to Examine the Agreement between Two Programs That Score Surveys Using Samejima's Graded Response Model Jim Penny source 7
WUSS 1999 A Logistic Regression on the Qualities of Educational Research: Characteristics of Publishable Articles Leping Liu,
George C. J. Fernandez		 source 5
WUSS 1999 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 6
WUSS 1999 Exploring Enzyme-Linked Immunosorbent Assay (ELISA) Data with the SAS Analyst Application Stewart L. Fossceco,
Nathan A. Curtis		 source 9
WUSS 1999 Modeling Individual Utility Functions Using SAS PROC LOGISTIC: A Case Study Using the NES Data Set Godofredo Gatus source 4
WUSS 1999 Polychotomous Logistic Regression and the Creation of Odds Ratios and Confidence Intervals Using the SAS PROC CATMOD Besa Nushi source 5
NESUG 1999 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud 7
NESUG 1999 Fitting Nonlinear Mixed Models with the New NLMIXED Procedure Russell D. Wolfinger 10
NESUG 1999 Input Selection, Performance Evaluation, and Credit Risk Analysis Shirley Tsai,
David Hamilton		 10
NESUG 1999 Modeling Customer Choice David R. Roberts 6
NESUG 1999 The missing link: PROC GENMOD Margaret Ann Goetz 3
MWSUG 1999 Using Logistic Regression to Predict Customer Retention Andrew H. Karp 5
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
SEUGI 1999 Comparison of various statistical techniques to support market retention and acquisition programs [presentation] Ian Thornhill source 66
SEUGI 1999 Datamarts without effort [presentation] Francis Steelandt,
Ignace Follet		 source 25
SEUGI 1999 Insurance application: marketing database, forecasting model and cluster design at Reale Mutura Assicurazioni Flavio Bonifacio source 16
SEUGI 1999 Internet as an evolutionary tool of the information system [presentation] Giovanni Maio source 30
SEUGI 1999 Issues in migrating from client/Server to a Web environment Alessandro Pozzoli,
Lindsay Walton,
Micaela Cera		 source 7
SUGI 1999 A SAS Macro to Analyze Data from a Matched or Finely Stratified Case-Control Design Robert A. Vierkant,
Terry M. Therneau,
Jon L. Kosanke,
James M. Naessens		 source 6
SUGI 1999 An Investigation of Quality-of-Life Issues in Patient Treatment Using Enterprise Miner Patricia B. Cerrito,
Kelly McMasters,
Vicki Viar		 source 6
SUGI 1999 Data Mining: An Overview of Methods and Techniques for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 7
SUGI 1999 Exploring Enzyme-Linked Immunosorbent Assay (ELISA) Data with the SAS Analyst Application Stewart L. Fossceco,
Nathan A. Curtis		 source 9
SUGI 1999 Fitting Nonlinear Mixed Models with the New NLMIXED Procedure Russell D. Wolfinger source 10
SUGI 1999 Generating Item Responses Based on Multidimensional Item Response Theory Jeffrey D. Kromrey,
Cynthia G. Parshall,
Walter M. Chason,
Qing Yi		 source 6
SUGI 1999 How to Display Correlated ROC Curves with the SAS System Barbara G. Schneider source 5
SUGI 1999 Use of Enterprise Miner and Multivariate Statistical Analysis to Build Fraud-Detection Models Shiao-Ping Lu source 5
MWSUG 1998 A Tour of UnifyPow: A SAS Module/Macro for Sample-Size Analysis Ralph G. O'Brien 11
MWSUG 1998 An Introduction to Binary Logistic Regression Linda M. Quinn 8
MWSUG 1998 If There Is A Will, There Is A Way: Getting Around Defaults of PROC LOGISTIC Chao-Ying Joanne Peng,
Tak-Shing Harry So		 10
MWSUG 1998 Producing ROC Curves For Discriminant Analysis Using PROC LOGISTIC Keith Dunnigan 7
WUSS 1998 Getting Started with PROC LOGISTIC Andrew H. Karp source 5
WUSS 1998 Use of Data Enterprise Miner & Multivariate Statistical Analysis to Build Fraud-Detection Models Shiao-Ping Lu source 7
NESUG 1998 A Smart Guide to Dummy Variables: Four Applications and a Macro Susan B. Garavaglia 10
NESUG 1998 Calculating Marginal Probabilities in PROC PROBIT Guy Pascale 5
NESUG 1998 Data Mining: An Overview of Methods and Techniques for Increasing Profits in Direct Marketing C. Olivia Parr Rud 8
NESUG 1998 Information Theory Makes Logistic Regression Special Ernest S. Shtatland 4
NESUG 1998 Multivariate Discrimination of Categorical Data Using the SAS System Jeffrey S. Butler 8
NESUG 1998 Strategies For Linking Medicaid Eligibility Files To Vital Statistics Birth Files To Assess The Efficacy Of Medicaid Managed Care Patrick J. Roohan 6
NESUG 1998 Using Logistic Regression to Predict Customer Retention Andrew H. Karp 5
NESUG 1998 Using SAS/IML to Calculate L-Moments for the Univariate Distribution Shape Parameters Skewness and Kurtosis Michael A. Walega 8
SESUG 1998 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud 7
SESUG 1998 Generating Item Responses Based on Multidimensional Item Respose Theory Jeffrey D. Kromrey,
Cynthia G. Parshall,
Walter M. Chason,
Qing Yi		 6
SESUG 1998 Modern Psychometric Theory: Using SAS to Calculate IRT Ability Estimates Jeffrey D. Kromrey,
Cynthia G. Parshall,
Walter M. Chason,
Qing Yi		 5
SESUG 1998 Software for GEE: PROC GENMOD and SUDAAN Babubhai V. Shah 5
PNWSUG 1998 Stepwise Anything: Reading in Regression Procedure Output for Further Testing Ted Clay,
Linda Remy		 10
SEUGI 1998 Data Warehouse+ Willem Wegman source 2
SEUGI 1998 Displaying and comparing correlated ROC curves with the SAS software Barbara G. Schneider source 8
SEUGI 1998 Pricing System of Alcoholic beverages in Finland by ALKO Inc. [presentation] Jorma Peltoia source 21
SEUGI 1998 Simulation of the probability of disease due to a toxic release Fuming Wu,
Huei Wang,
Raymond A. Gibley,
Wanzer Drane		 source 5
SUGI 1998 A Tour of UnifyPow: A SAS Module/Macro for Sample-Size Analysis Ralph G. O'Brien source 10
SUGI 1998 An Information-Gain Measure of Fit in PROC LOGISTIC Ernest S. Shtatland,
Mary B. Barton		 source 6
SUGI 1998 Finding the Solution to Data Mining: A Map of the Features and Components of SAS Enterprise Miner Software Table of Contents John C. Brocklebank,
Mark Brown		 source 24
SUGI 1998 How to Use SAS Software to Evaluate Screening Tests Using Predictive Values in Conjunction with ROC Curves Richard Severino source 6
SUGI 1998 Multivariable Cox Proportional Hazard Model by SAS PHREG &Validation by Bootstrapping Using SAS Randomizers With Applications Involving Electrocardiology and Organ T ransplants Sudhanshu K. Ghoshal source 6
SUGI 1998 Reporting Results of Multiple Logistic Regression Models Depending on the A vailability of Data Richard M. Mitchell source 5
SUGI 1998 Robust Standard Error Estimate for Cluster Sampling Data: A SAS/IML Macro Procedure for Logistic Regression with Huberization Honghu Liu source 6
SUGI 1998 Strategic Use of the SAS System in Banking Laurie Rose source 5
SCSUG 1997 Reliability Data Analysis in the SAS System Gordon Johnston 10
WUSS 1997 How to Use the SAS Software to Evaluate Screening Tests Using Predictive Values in Conjunction with ROC Curves Richard Severino source 7
WUSS 1997 Logistic Regression Modeling in Complex Surveys: Examples Using LOGITSE SAS Macro Jesse Albert Canchola,
Brian D. Marx,
Joseph A. Catania		 source 10
WUSS 1997 Population Adjusted Kaplan-Meier Curves Jimmy Thomas Efrid source 6
WUSS 1997 Reliability Data Analysis in the SAS System Donna O. Fulenwider,
Gordon Johnson		 source 10
WUSS 1997 Some Applications of Data Mining Tools in Database Marketing Jiamin Liu source 6
NESUG 1997 Bootstrapped Confidence Intervals for Parametric Estimates of Survival Distribution Function Vadim Pliner 8
NESUG 1997 Information as a Unifying Measure of Fit in SAS Statistical Modeling Procedures Ernest S. Shtatland 7
NESUG 1997 Survival Analysis: Non-Traditional Applications for Non-Statisticians Margaret Ann Goetz 5
NESUG 1997 Using Logistic Regression to Test for Interaction in the Presence of Zero Cells Lisa Suchower 10
MWSUG 1997 Enhancing the Output from PROC LOGISTIC Bharat K. Thakkar,
Kwan Hur,
Charles A. Oprian,
William G. Henderson,
Sharon Urbanski		 6
MWSUG 1997 Reliability Data Analysis in the SAS System Gordon Johnston 10
SESUG 1997 Modeling Higher Education Processes with Multiple Logistic Regression: SAS Examples Daniel Teodorescu 5
SESUG 1997 Summarization of LOGISTIC Regression Results When There Are Many Dependent Variables and a Common Shared Set of Explanatory Variables John T. Jones 7
SEUGI 1997 What is the profit on Spanish corn sold in Germany? Management information and sales reporting at Danisco Kjeld Andreasen source 2
SUGI 1997 An Experiment Analysis System for Fixed and Random Effects in OverDispersed Binomial Responses in Semiconductor Manufacturing Jose G. Ramirez,
Randall S. Collica		 source 6
SUGI 1997 Creating Clinical Trial Summary Tables Containing P-Values: A Practical Approach Using Standard SAS Macros Jun Zuo,
Carl R. Haske		 source 6
SUGI 1997 Developing Risk Adjustment Techniques Using the SAS System for Assessing Health Care Quality in the IMSystem Yanchun Xu,
Andrius Kubilius		 source 5
SUGI 1997 Enhancing the Output from PROC LOGISTIC Bharat K. Thakkar,
Kwan Hur,
Charles A. Oprian,
William G. Henderson,
Sharon Urbanski		 source 6
SUGI 1997 Getting Started with PROC LOGISTIC Andrew H. Karp source 5
SUGI 1997 How Many Good Fishes Are There in Our Net? Neural Networks as a Data Analysis Tool in CDE-Mondadori's Data Warehouse Guido Cuzzocrea,
Alberto Saccardi,
Giovanni Lux,
Emilia Porta,
Arianna Benatti		 source 6
SUGI 1997 Logistic Regression Adjustment of Proportions and its Macro Procedure Dingyi Zhao source 6
SUGI 1997 LOGITSE: A SAS Macro for Logistic Regression Modeling in Complex Surveys Jesse Albert Canchola,
Brian D. Marx,
Joseph A. Catania		 source 6
SUGI 1997 Net Impact Analysis for Program Evaluation, Modeling and SAS Programming Boqing Wang source 6
SUGI 1997 Odds Ratios in a Tabular Presentation Stephen M. Noga,
Dingyi Zhao		 source 6
SUGI 1997 UnifyPow: A SAS Macro for Sample-Size Analysis Ralph G. O'Brien source 6
SCSUG 1996 Getting Started with PROC LOGISTIC Andrew H. Karp 5
SESUG 1996 Generalized Linear Models (Logistic Regression), a Comparison of Available Options and Output from the Catmod, Probit, Logistic, and Genmod Procedures Margaret A. Connolly 6
SESUG 1996 Getting Started with PROC LOGISTIC Andrew H. Karp 5
SESUG 1996 Quasicomplete Separation in Logistic Regression: A Medical Example Madeline J. Boyle 6
NESUG 1996 Between a ROC Curve and a Hard Place Joseph Kufera,
Kimberly A. Mitchell		 10
MWSUG 1996 Testing for and Correcting for Missing Data in Survey Responses: A Survey of Downtown Business in Minneapolis Shelly S. Baxter,
Lawrence C. Marsh		 10
WUSS 1996 Logistic Regression: From SAS Coding to Statistical Interpretation Elizabeth Li source 9
PNWSUG 1996 Net Impact for Program Evaluation Modeling and SAS Programming Boqing Wang 18
SEUGI 1996 Computational Changes in the Analysis of Data from Case-Control Studies Anette Pfahlberg,
Olaf Gefeller		 source 9
SEUGI 1996 Diagnostics and estimation procedures of quantitative dose response relationships using SAS ... Hans-Peter Altenburg source 8
SEUGI 1996 Experimental analysis and SAS/EIS - reporting in semiconductor manufacturing Randall S. Collica source 29
SEUGI 1996 The growing need for distributed data warehouse systems due to globalisation of business Ger Scherpens,
Sander Piet		 source 12
SUGI 1996 A SAS Macro for validating a Logistic Model with Split Sample and Bootstrap Methods Kwan Hur,
Charles A. Oprian,
William G. Henderson,
Sharon Urbanski,
Bharat K. Thakkar		 source 4
SUGI 1996 Conjoint and Beyond: Clustering and Logistic Regression of Attribute Importance Leslie A. Christensen source 5
SUGI 1996 Estimating Optimal Transformations in Non-Linear Models Nicholas Scott Cardell,
Dan Steinberg		 source 6
SUGI 1996 New Age Marketing: Past Life Regression vs. LogisticRegression C. Olivia Parr Rud source 5
SUGI 1996 Teaching the SAS System as an Academic Subject Andrew H. Karp source
SUGI 1996 Using the SAS System to Detect Differential Item Functioning James A. Penny source 6
MWSUG 1995 An Introduction to Binary Logistic Regression Linda M. Quinn 8
NESUG 1995 Getting Started with PROC LOGISTIC Andrew H. Karp 5
NESUG 1995 More on PROC PHREG: Conditional Logistic Regression (or Should We Do Another Study..?) Peter P. Gaccione 10
SESUG 1995 Getting Started with PROC LOGISTIC Andrew H. Karp 5
SESUG 1995 New Age Marketing: Past Life Regression versus Logistic Regression C. Olivia Parr Rud 9
SUGI 1995 A Comparative Evaluation of Selected Statistical Software for Computing Various Categorical Analyses Nancy McDermott,
Cynthia White		 source 6
SUGI 1995 A Primer on Poisson Regression using SAS/STAT Software Methods and Interpretation of Results Richard McNally source 4
SUGI 1995 An Interactive Program of the GEE Method for the Analysis of Longitudinal Data Masakazu Andoh,
Tohru Uwoi		 source 6
SUGI 1995 Analytic Modeling in SAS/INSIGHT Software Heman Robinson source 6
SUGI 1995 Bartlett Correction of Binomial Regression Analyses of Small Samples Lawrence Moulton,
Roy St. Laurent,
Lisa Weissfeld		 source 5
SUGI 1995 Plots for Influential and Poorly Fitting Covariate Patterns in the LOGISTIC Procedure Mark Moss,
William C. Murphy		 source 6
SUGI 1995 Using the SAS System for Market Research Laurie Rose source 5
SUGI 1995 Writing Programs for JMP Software Alvin M. Best III source 6
SCSUG 1994 On A More Efficient Use of Quadratic Logistic Discrimination Dean M. Young,
Samuel L. Seaman		 3
NESUG 1994 If We Can't Trust SAS Who Can We Trust? -or- Just How Accurate is Your Computer? John J. Cohen 10
NESUG 1994 PROC LOGISTIC: A Form of Regression Analysis Edith Flaster 3
SESUG 1994 Critical Steps to Meaningful Causal Data Analysis Deborah Wall 4
SESUG 1994 GRAPHICS USING GRAPmCS TO CONVEY STATISTICAL CONCEPTS AND RESULTS David A. Dickey 4
SESUG 1994 Managing Overkill: How to Use SAS PROC CA TMOD to Estimate Odds Ratios Sigurd W. Hermansen 5
SESUG 1994 rOSTERS Applications in SASIIMLTM: Getting Odds Ratios from PROC LOGISTIC D. Kim Chantala 5
SESUG 1994 SAS 6.09 on a CONVEX C3840: First Impressions Kenneth A. Hardy,
Sally M. Muller		 8
SEUGI 1994 Analysis of Dose-response Curves in Molecular Pharmacology by the SAS System Paola Baiardi source 10
SEUGI 1994 Mainframe Rightsizing Consider the RISCs Don Lutter source 9
SEUGI 1994 Neural Networks and Statistical Models Derek Powell,
Martin Duffy		 source 9
SEUGI 1994 Practical Aspects of Modelling Techniques in Logistic Regression Procedures of the SAS System Josef Hogel source 6
SEUGI 1994 Resampling Using SAS/IML Software Some Medical Applications Gina Joubert source 8
SEUGI 1994 The Analysis of Data from Case-control Studies Which Statistical Methods and Software Products are Used Olaf Gefeller source 9
SUGI 1994 Interpreting Complex Nonlinear Models Lawrence C. Marsh,
Maureen A. McGlynn,
Debopam Chakraborty		 source 5
SUGI 1994 Neural Network Implementation in SAS Software Warren S. Sarle source 23
SUGI 1994 Neural Networks and Statistical Models Warren S. Sarle source 13
SUGI 1994 SAS 6.09 on a CONVEX C3840: First Impressions Kenneth A. Hardy,
Sally M. Muller		 source 8
NESUG 1993 Use of PROC IML to Calculate L-Moments for the Univariate Distributional Shape Parameters Skewness and Kurtosis Michael A. Walega 8
WUSS 1993 A Macro for Personalized Odds Ratios Alice Pressman,
K. L. Stone,
Estie Hudes,
J. L. Tao		 source 4
WUSS 1993 A SAS Macro to Test the Significance of Differences Between Parameter Estimates in PROC CATMOD Linda R. Ferguson,
Mel Widawski,
Dawn M. Upchurch		 source 5
WUSS 1993 The LACKFIT Option in SAS LOGISTIC Procedure: A Problem and a Proposed Solution Estie Hudes,
L. Palermo		 source 2
SEUGI 1993 Generalised Linear Model and Correspondence Analysis. Application to the Goitre Problem in Mali Michel Tenehaus source 28
SEUGI 1993 Variable Selection Techniques Implemented in Procedures of SAS Software Olaf Gefeller source 9
SUGI 1993 A SAS Macro Procedure to Summarize the Output of Logistic Regression Models Benjamin I. Broder,
Chih-Ping Chou		 source 6
SUGI 1993 A Tutorial on Logistic Regression Ying C. So source 6
SUGI 1993 Application of Conditional Logistic Regression Methods to Event History and Multiple-Choice Data Dan Steinberg source 4
SUGI 1993 Bayesian Statistics in SAS Software William J. Raynor Jr. source 10
SUGI 1993 Macros Can Condense and Simplify Statistical Output for Presentation to End Users John A. Wenston 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
SESUG 1993 Developing an In-House SAS Training Program for an Academic Environment Barbara B. Okerson 5
SCSUG 1992 Calculating the Odds Ratio and Goodness of Fit Statistics for Logistic Regression Models Using PROC LOGISTIC Chuan-Chuan C. Wun 19
NESUG 1992 Principles of Logistic Regression in Radiation Oncology J. Efird,
F. Pardo,
P. Okunieff,
D. Shoenfeld,
H. Aronen		 10
NESUG 1992 Using Macros to Create Parameter Driven Procedures that Summarize and Present Statistical Output in Tabular form John Wenston,
Ralph D'Agostino Jr.,
Kimbery Dukes		 6
SEUGI 1992 Effects of Using the NOINT Option in Various Regression Procedures of SAS/STAT Software Rainer Muche source 10
SEUGI 1992 The Need for Control Systems in a Decentralised Organisation Patrick Amzallag source 21
SEUGI 1992 The SAS System and Meta Knowledge Herve Augendre source 7
SEUGI 1992 Using SAS/IML for Advanced Statistical Needs, With an Example on Credit Scorecard Building Lynne Pritchard,
Paul Chik		 source 10
SUGI 1992 Marginal. Probabilities: An Intuitive Alternative to Logistic Regression Coefficients P. M. Wright source 3
NESUG 1991 Tests for Normality and Other Goodness-of-Fit Tests Ralph D'Agostino Jr.,
Albert J. Belanger		 7
SEUGI 1991 Future Directions of Statistical Software with the SAS System David M. DeLong source 20
SUGI 1991 A Macro For Principal Component Logistic Regression Brian D. Marx source 6
SUGI 1991 A Program to Compute Odds Ratios and Confidence Intervals from LOGISTIC Output Eric Yu I. Wang,
Aili Gong,
David P. MacKinnon		 source 4
SUGI 1991 The TPSTAT System for the Analysis and Reporting of Toxicology and Pathology Data and Its Implementation under Version 6 of the SAS Software Murray R. Selwyn,
Peter P. Gaccione,
Scott E. Moseley,
Virginia M. Ashe		 source 6
SEUGI 1990 Directions of the SAS System in Statistics and Decision Support Gerhard Held source 7
SEUGI 1990 Robust Estimation of Radio Immuno Assays Using SAS Software Hans-Peter Altenburg source 8
SUGI 1990 Using Release 6.06 of the SAS System for Marketing Information Systems Mike Senger,
Richard Roach		 source 6
SEUGI 1989 Release 6.06 Of The SAS System Under VMS Hints And Recommendations To Help You Adjust Colin J. Harris source 15
SUGI 1989 A Concise and Easily Interpretable Way of Presenting the Results of Regression Analyses Stephen Senzer,
Leland Stewart		 source 4
SUGI 1989 Weighted Multicollinearity Diagnostics for Logistic Regression Brian D. Marx,
Eric P. Smith		 source 6
SUGI 1988 Detection of Influential Observations in Logistic Regression Using the SAS System Charles S. Davis source 6
SUGI 1988 Estimating Logistic Regression Models When the Dependent Variable Has No Variance Nicholas Scott Cardell,
Dan Steinberg		 source 2
SUGI 1988 Partial Proportional Odds Models and the LOGIST Procedure Frank E. Harrell Jr.,
Bercedis L. Peterson		 source 5
SUGI 1988 PROC DICHOT: A SAS Procedure for Creating Dichotomous Variables Paul Lambert source 6
SUGI 1988 Processing and Analyzing Supermarket Scanner Data Charles F. Hofacker source 6
SEUGI 1987 A Statistical Look at Risk Factors for Coronary Heart Disease David J. Shannon source 15
SUGI 1987 Graphical Methods for the Analysis of Survival Data Frank E. Harrell Jr.,
Kerry L. Lee,
Barbara G. Pollock		 source 9
SUGI 1987 Interpretation and Diagnostics of the Multinomial Logistic Regression Dan Steinberg source 6
SUGI 1987 Use of SAS Procedures for Estimating Design Based Logistic Regression Variances by Balanced Repeated Replication (BRR) Daniel H. Freeman Jr.,
Philip J. Leaf,
Martha Livingston Bruce		 source 5
SUGI 1986 Repeated Application of a Generalized Logistic Model To Count Data Using PROC MATRIX Linda Williams Pickle source 6
SUGI 1986 Spline Functions for Logistic Regression Modeling Thomas F. Devlin,
Barbara J. Weeks		 source 6
SUGI 1985 A SAS Macro for Logistic Discrimination for Three or More Populations James E. Dunn,
Eileen C. King		 source 6
SUGI 1985 A SAS Procedure for Smoothing Generalized Linear Models William J. Raynor Jr. source 6
SUGI 1985 Application of the SAS System to Regression Analysis of Tumor Prevalence Data Saeid B. Amini,
Debra L. Palka		 source 7
SUGI 1985 Comparison of Multivariate Regression Analysis between Logistic Model and the Least Square Model Using SAS Software Jane R. Bratz,
J.S. Kim,
George K. Tokuhata		 source 4
SUGI 1985 Conditional Logistic Regression in SAS Software Gregg A. Drube,
Dale F. Kraemer,
Stephanie Van Lier		 source 7
SUGI 1985 MACRO LGTREG for Logistic Regression and Associated Diagnostics Thomas F. Devlin,
Barbara J. Weeks		 source 6
SUGI 1985 PROC PECAN - A SAS Procedure Sivaram Kalyandrug,
Jay H. Lubin		 source 8
SUGI 1985 The Practical Value of Logistic Regression Frank E. Harrell Jr.,
Kerry L. Lee		 source 6
SUGI 1985 The Role of Invertibility and Symmetry of Polychotomous Metameters for GSK and Other Related Analyses James E. Dunn source 10
SUGI 1984 A General Woolf-Haldane/Mantel-Haenszel Procedure for Dichotomous or Polychotomous Exposure Variables and for Any Number of Stratification/Control Variables Philip N. Gallagher Jr.,
William M. Haenszel		 source 3
SUGI 1984 Graphic Representation of Logistic Regression Models John K. Gohagan,
Donald G. Sessions,
Edward L. Spitznagel		 source 4
SUGI 1984 Logistic Regression and Associated Diagnostics using PROC MATRIX Thomas F. Devlin source 6
SUGI 1983 Offense, Defense, and Causality in Baseball Charles F. Hofacker source 5
SUGI 1983 Overview of Categorical Data Analysis Methods Ingrid A. Amara,
Susan Atkinson,
Gary G. Koch,
William M. Stanish		 source 10
SUGI 1983 PROC MCSTRAT Susanne L. Daood,
James M. Naessens,
Kenneth P. Offord		 source 8
SUGI 1982 A General Linear Models Approach for Comparing the Response of Several Species in Acute Toxicity Tests Karen L. Daniels,
Michael P. Farrell,
Jonathan C. Goyert,
Rodney H. Strand		 source 6
SUGI 1982 Applications of SAS (FUNCAT) to the Analysis of Epidemiological/Survivorship Data Saeid B. Amini,
William R. Clarke,
Robert F. Woolson		 source 9
SUGI 1982 SAS/GRAPH and the Geometryof Statistical Distributions Charles F. Hofacker,
Paul Hoffman		 source 8
SUGI 1980 An Overview of Survival Analysis and SAS Irving K. Hwang source
SUGI 1980 Automated Analysis of Case-Control Data Using the SAS System Elizabeth J. Martin,
Linda Williams Pickle		 source 6
SUGI 1980 Procedures for Large Regression Problems Requiring Maximum Likelihood Estimation Frank E. Harrell Jr.,
Kerry L. Lee,
Ray A. McKinnis		 source 4
SUGI 1980 Recent Developments in BMDP of Special Interest to SAS Users James W. Frane source 8
SUGI 1979 A Class of Invertible Functions for Analysis of Categorical Response Using Linear Models Gloria Cappy,
James E. Dunn		 source 6
SUGI 1978 Maximum Likelihood Estimation of the Multivariate Logistic Martha McCrate,
Michael A. Province,
Reimut Wette		 source 3

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