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435 papers and 6 presentations; 404 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
PharmaSUG 2026 Regression Analysis Made Easy Using R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 source 32
MWSUG 2025 Conducting Survival Analysis in SAS using Medicare Claims as a Real-world data source Jayanth Iyengar source 32
MWSUG 2025 Highlighting the Differences: PROC COMPARE in Excel Jane Eslinger source 12
WUSS 2025 Conducting Survival Analysis in SAS using Medicare Claims as a Real-world data source Jayanth Iyengar source 32
WUSS 2025 Highlighting the Differences: PROC COMPARE in Excel Jane Eslinger source 12
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 Conducting Survival Analysis in SAS using Medicare Claims as a Real-world data source Jayanth Iyengar source 32
PharmaSUG 2025 SAS Macro to Calculate Standardized Mean Difference Tong Zhao,
Tian Gu		 source 2
MWSUG 2024 SAS PROC GEOCODE By Example: A Case Study [presentation] Louise S. Hadden source 44
SESUG 2024 Comparative Analysis of Sentiment Prediction Methods Using Aspects in Consumer Reviews Harish Palakurthi source 8
WUSS 2024 From Keys to Credit: A Deep Dive into Homeownership's Influence on Loan Quality Yi-Che Chen,
Debanik Chakraborty		 source 15
SESUG 2023 Binning Predictors for Logistic Regression Bruce S. Lund source 25
PharmaSUG 2023 Introduction of Developing Resistance Dataset Jenny H. Zhang,
Shunbing Zhao		 source 6
PharmaSUG 2023 Repetitive Analyses in SAS - Use of Macros Versus Data Inflation and BY Group Processing Bradford J. Danner,
Indrani Sarkar		 source 6
PharmaSUG 2023 Survival Methods for Crossover in Oncology Trials Brian Mosier source 18
SESUG 2022 Don't be so One-Dimensional: How to Engineer Multi-Dimensional, High Cardinality Categorical Inputs for Machine Learning Aleksandar Nikolic source 25
SESUG 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
SESUG 2021 Using PROC SQL to restructure data for common healthcare applications Tamar C. Roomian source 6
SESUG 2021 Utilizing SAS MACROS to Deduplicate Your Data William Zachary Smith source 10
PHUSE US 2021 Software Demonstration: SD14: Accelerate your Transition to R in 4 months: Learn About the Accel2R Accelerated Learning Program [presentation] Steve Walker source 22
PharmaSUG 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source 21
SGF 2021 Back to Basics: Running an Analysis from Data to Refinement in SAS Deanna Naomi Schreiber-Gregory source
presentation		 20
SGF 2021 Detecting Online Recruitment Fraud Matthew Fruge,
Noah Heinrich		 source 6
SGF 2021 Harnessing Raw Data From Submission Forms to Gauge Food Insecurity Among College Students Zoraya Cruz-Bonilla source
presentation		 12
SGF 2021 Haste Makes Waste: Don't Ruin Your Reputation With Hasty Regression Steven C. Myers source
presentation
sas		 39
SGF 2021 Weight of Evidence, Dummy Variables and Degrees of Freedom Bruce S. Lund source
sas
presentation
pdf		 15
SESUG 2020 Outlier Detection and Treatment Ross Bettinger source 29
SGF 2020 Applications of DOW Loop: Extension to PROC REPORT Neha Yadav,
Pranathi Salla		 source 11
SGF 2020 Don't Just Survive, Thrive with This Multi-Stratified Cox Proportional Hazards Model Macro Katelyn J. Ware,
Paul Egeler		 source 11
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 Moving from Messy Data to a Clean Analytic Dataset: Common Data Management Techniques using SAS Bethany A. Bell source 27
SGF 2020 Neural-Network Based Forecasting Strategies in SAS Viya Steven C. Mills source 14
SGF 2020 The Plight of the Honeybee Rachael Bishop source 8
SGF 2020 Time After Time: D-I-D and ITS Models in SAS Margaret Warton source 21
PharmaSUG 2020 %MVMODELS: a Macro for Survival and Logistic Analysis Jeffrey P. Meyers source 23
PharmaSUG 2020 Making Customized ICH Listings with ODS RTF Huei-Ling Chen,
William Wei		 source 13
SESUG 2019 A user-friendly and robust macro that produces a publication-ready Table 1 Daniel Brinton,
Marshall Chew		 source 5
SESUG 2019 Advanced Statistical Modeling within Machine Learning and Big Data Analytics Niloofar Ramezani,
George Mason		 source 16
SESUG 2019 Data-driven Programming Techniques using SAS Macros to Semi-automate Generation of Descriptive Tables in Healthcare Research Katie Mercaldi source 37
SESUG 2019 Intervention at the Library: Assessing the Effect of an Event David A. Dickey,
John Vickery		 source 19
SESUG 2019 Macros for Neural Network Modeling Ross Bettinger source 23
SESUG 2019 The Best of Both Worlds: Forecasting Using Time Series with Inputs David A. Dickey source 19
SCSUG 2019 Building a Neural Network Scoring Model with JMP and Microsoft SQL Server to predict Aggregate Motor Vehicle Driver Alcohol Impairment David Whitchurch source 10
SCSUG 2019 Do you know when your data is lying to you? The HOW of Regression Analysis with Quantitative and Qualitative Variables Steven C. Myers source 20
SCSUG 2019 Explore your data before you rush to analysis, you will thank me later: explorations in cross section data Steven C. Myers source 28
MWSUG 2019 Explore your data before you rush to analysis, you will thank me later: explorations in cross section data Steven C. Myers 28
MWSUG 2019 Logistic Regression, Basics and Beyond Bruce S. Lund 35
MWSUG 2019 Surviving Survival Analysis 101: Making the Likelihood Ratio Test Easier Using a Macro Katelyn Ware,
Rachel Baxter		 13
PharmaSUG 2019 Sum Fun with Flags! Sum Any Flagged Occurrence Data with FLAGSUM and Report It with FLAGRPT or PROC REPORT. Brendan Bartley source 31
SGF 2019 A Selective Overview of Panel Data with Applications in SAS Oral Capps source 26
SGF 2019 Asking the Right Questions: A study in Multiclass Imbalanced Classification for H1-B Visas Matthew Bunker,
Owen Kelly		 source 7
SGF 2019 Does the Cat Have Your Tongue? Using the VNAME and CATX Functions to Get SAS to Speak to You Gayle K. Springer source 6
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 Railways Scheduling and Rolling Stock Optimization using SAS Ayush Tiwari,
Lokendra Devangan,
Malleswara Sastry Kanduri,
Faiz Hamid		 source 10
SGF 2019 Screening, Transforming, and Fitting Predictors for the Cumulative Logit Model Bruce S. Lund source 21
SGF 2019 The Best of Both Worlds: Forecasting Using Time Series with Inputs David A. Dickey source 19
SGF 2019 Transforming Ordinal Predictors to Numeric For Binary Logistic Models Bruce S. Lund source 10
SGF 2019 Using ETS Software for Intervention Models for Sales of Cars Anders Milhøj source 8
SGF 2019 Using SAS Procedures to Fit Rasch Models Tianshu Pan,
Yumin Chen		 source 10
SCSUG 2018 Are you in danger of stroke? An insight into the leading causes. Anjali Bansal source 17
PHUSE EU 2018 Collinearity in Mixed Models John Hendrickx source
presentation source		 11
SESUG 2018 Comparison of SAS, SAS University Edition, and Microsoft Excel in Collaborative Data Analysis of Physical Properties by Scientist and Statistical Programmer John Schreiber source 6
SESUG 2018 Computing Predicted Values and Residuals from Data Sets Containing Classification Variables with Large Numbers of Levels Jamie McClave Baldwin,
Ramon C. Littell,
James McClave		 source 14
SESUG 2018 Emulating FIRST. and LAST. SAS DATA Step Processing in SQL? Concepts and Review Thomas E. Billings source 8
SESUG 2018 Forecasting: Something Old, Something New David A. Dickey source 26
MWSUG 2018 Automatic Indicators for Dummies: A macro for generating dummy indicators from category type variables Matthew Bates 11
MWSUG 2018 Creating suicide attempt/intentional self-harm episodes using administrative billing data Jamie Kammer,
Mahfuza Rahman,
Qingxian Chen		 7
MWSUG 2018 Screening, Transforming, and Fitting Predictors for Cumulative Logit Model Bruce S. Lund 22
MWSUG 2018 What's the Difference? Using the PROC COMPARE to find out. Larry Riggen 19
WUSS 2018 Defensive programming techniques for ongoing clinical studies Bharath Donthi,
Lingjiao Qi		 source 8
WUSS 2018 Forecasting: Something Old, Something New David A. Dickey source 26
WUSS 2018 Oops, I D-I-D It Again! Advanced Difference-in-Differences Models in SAS Margaret Warton,
Melissa Parker		 source 17
PharmaSUG 2018 R 101: The base Package Arthur X. Li source 22
PharmaSUG 2018 Summary Table for Displaying Results of a Logistic Regression Analysis Lori S. Parsons source 9
PharmaSUG 2018 Utilize Dummy Variables/Datasets in Graph Generation Amos Shu source 4
SGF 2018 Confirmatory Factor Analysis in SAS : Application to Public Administration Niloofar Ramezani source 13
SGF 2018 Forecasting: Something Old, Something New David A. Dickey source 26
SGF 2018 Getting More Insight into Your Forecast Errors with the GLMSELECT and QUANTSELECT Procedures Gerhard Svolba source 26
SGF 2018 Using Arrays to Quickly Perform Fuzzy Merge Look-ups: Case Studies in Efficiency Arthur L. Carpenter source 14
SESUG 2017 Binning of Predictors for the Cumulative Logit Model Bruce S. Lund source 20
SESUG 2017 Tornado Inflicted Damages Pattern Vasudev Sharma source 11
SESUG 2017 Using Categorical Variables in Regression Analysis Jonas V. Bilenas source 15
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
PHUSE EU 2017 Health Technology Assessment PRO Least Squares Mean Difference Programming Techniques Using SAS Yirong Cao,
Siory Traoré		 source
presentation source		 11
PHUSE EU 2017 Power Calculation in a Multi-Stage Design, Following the Main Endpoint, A Binomial DIstribution Antonio Nieto,
Javier Gómez		 source
presentation source		 4
MWSUG 2017 An Array of Possibilities: Manipulating Longitudinal Survey Data with Arrays Lakhpreet Gill 8
MWSUG 2017 Correcting for Selection Bias in a Clinical Trial Shana Kelly 8
MWSUG 2017 Logistic Model Selection with SAS PROC's LOGISTIC, HPLOGISTIC, HPGENSELECT Bruce S. Lund 18
MWSUG 2017 Tornado Inflicted Damages Pattern Vasudev Sharma 11
WUSS 2017 Maintaining ORDER in Your (PROC) Reporting Bill Coar source 5
PharmaSUG 2017 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 20
SGF 2017 A SAS Macro for Covariate Specification in Linear, Logistic, and Survival Regression Sai Liu source
zip		 19
SGF 2017 Construction of a Disease Network and a Prediction Model for Dementia Jinwoo Cho source 14
SGF 2017 Estimation Strategies Involving Pooled Survey Data Taylor Lewis source 15
SGF 2017 Fitting Complex Statistical Models with NLMIXED and MCMC Procedures Robin High source 14
SGF 2017 KA Team: Crime in the City of Philadelphia Edwin Baidoo,
Christina Jones,
Muniza Naqvi		 source 21
SGF 2017 Methods of Multinomial Classification Using Support Vector Machines Ralph Abbey,
Taiping He,
Tao Wang		 source 20
SGF 2017 The ODS EXCEL statement: Tips and Tricks for the TABULATE and REPORT Procedures Richard A. DeVenezia source 17
SGF 2017 Weight of Evidence Coding for the Cumulative Logit Model Bruce S. Lund source 15
SCSUG 2016 Show Me the Money! Text Analytics for Decision-Making in Government Spending Tom Sabo source 13
SESUG 2016 A Simple SAS Macro to Perform Blinder-Oaxaca Decomposition Taylor Lewis,
Stanislas Ezoua		 source 8
MWSUG 2016 A Demonstration of Various Models Used in a Key Driver Analysis Steven M. LaLonde 17
MWSUG 2016 Fitting Complex Statistical Models with PROCs NLMIXED and MCMC Robin High 16
MWSUG 2016 Regression Analysis of the Levels of Chlorine in the Public Water Supply in Orange County, FL Drew Doyle 11
WUSS 2016 An Animated Guide: Incremental Response Modeling in Enterprise Miner Russell M. Lavery source 19
WUSS 2016 Getting it Done with PROC TABULATE Michael Williams source 14
WUSS 2016 Introduction to Survival Analysis in SAS Leanne Goldstein,
Rebecca Ottesen		 source 19
PharmaSUG 2016 Capturing Macro Code when Debugging in the Windows Environment: The Power of MFILE and the Simplicity of Pasting Kevin R. Viel source 10
PharmaSUG 2016 What makes a "Statistical Programmer" different from a "Programmer" Arun Raj Vidhyadharan,
Sunil Mohan Jairath		 source 12
SGF 2016 Exploring the Factors That Impact Injury Severity Using Hierarchical Linear Modeling (HLM) Pradeep Bista source 9
SGF 2016 Finding and Evaluating Multiple Candidate Models for Logistic Regression Bruce S. Lund source
zip		 17
SGF 2016 Holiday Demand Forecasting in the Utility Industry Rain Xie,
Alex Chien		 source 13
SGF 2016 Predicting Forest Fire Occurrence and Incremental Fire Rate Using SAS 9.4 and SAS Enterprise Miner 14.1 Quyen Nguyen source 12
SGF 2016 Probability Density for Repeated Events Bruce S. Lund source 16
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 Using a Single SAS Radar Graphic to Describe Multiple Binary Variables within a Population Kevin Sundquist,
Jacob E Julian,
Faith Parsons		 source
zip		 8
SGF 2016 Using SAS Arrays to Calculate Bouts of Moderate to Vigorous Physical Activity from Minute-by-Minute Fitbit Data Faith Parsons,
Keith M Diaz,
Jacob E Julian		 source
zip		 13
SCSUG 2015 Using SAS Software to Determine the Mixed Rotterdam Demand Model Parameters and Test Statistical References Maryam Tabarestani,
Hector Zapata		 23
MWSUG 2015 Fitting and Evaluating Logistic Regression Models Bruce S. Lund 26
PHUSE EU 2015 How to Reduce Bias in the Estimates of Count Data Regression Ashwini Joshi,
Sumit Singh		 source
presentation source		 10
SESUG 2015 All Data Are (Most Likely) Not Created Equal: A SAS Macro to Compare Structure and Data Across Multiple Datasets Jason L. Salemi source 13
SESUG 2015 Probability Density for Repeated Events Bruce S. Lund source 16
SESUG 2015 Regression Analysis of the Levels of Chlorine in the Public Water Supply in Orange County, FL Drew Doyle source 10
SESUG 2015 Using PROC MEANS to summarize months of eligibility for Medicaid beneficiaries John Wedeles,
Colleen Sonosky		 source 4
WUSS 2015 A strategy for understanding your data: Binary Flags and PROC MEANS Tejaswini Tiruke,
Glen Masuda		 source 5
WUSS 2015 Using Arrays to Quickly Perform Fuzzy Merge Look-ups: Case Studies in Efficiency Arthur L. Carpenter source 14
PharmaSUG CN 2015 Creating Binary Tables in a Snap Yan Liu 5
PharmaSUG 2015 Is Your Failed Macro Due To Misjudged "Timing"? Arthur X. Li source 22
PharmaSUG 2015 Using Arrays to Quickly Perform Fuzzy Merge Look-ups: Case Studies in Efficiency Arthur L. Carpenter source 14
SGF 2015 Analyzing Customer Answers in Calls from Collections Using SAS Text Miner to Respond in an Efficient and Effective Way Oscar Ayala,
Jenny Lancheros		 source 6
SGF 2015 Data Sampling Improvement by Developing the SMOTE Technique in SAS Lina Maria Guzman Cartagena source 9
SGF 2015 Functional Modeling of Longitudinal Data with the SSM Procedure Rajesh S. Selukar source
zip		 19
SGF 2015 How to Use SAS for GMM Logistic Regression Models for Longitudinal Data with Time-Dependent Covariates Katherine Cai source 8
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 Predicting Readmission of Diabetic Patients Using the High-Performance Support Vector Machine Algorithm of SAS Enterprise Miner 13.1 Hephzibah Munnangi,
Goutam Chakraborty		 source 10
SGF 2015 Reducing the Bias: Practical Application of Propensity Score Matching in Healthcare Program Evaluation Amber Schmitz,
Jessica Navratil-Strawn,
Stephen Hartley,
Ronald J. Ozminkowski		 source 7
SGF 2015 Show Me the Money! Text Analytics for Decision-Making in Government Spending Tom Sabo source 13
SGF 2015 Sums of Squares: The Basics and a Surprise Sheila Barron,
Michelle Mengeling		 source 8
SGF 2015 The bookBot Ultimatum! David A. Dickey,
John Vickery		 source
zip		 19
SGF 2015 Using and Understanding the LSMEANS and LSMESTIMATE Statements David J. Pasta source 20
SGF 2015 Want an Early Picture of the Data Quality Status of Your Analysis Data? SAS Visual Analytics Shows You How Gerhard Svolba source
zip		 19
SGF 2015 When Two Are Better Than One: Fitting Two-Part Models Using SAS Laura Kapitula source 9
SESUG 2014 %DISCIT Macro: Pre-screening continuous variables for subsequent binary logistic regression analysis through visualization Mohamed S. Anany source 6
SESUG 2014 Analysis of Zero Inflated Longitudinal Data Using Proc NLMIXED Delia C Voronca,
Leonard E Egede,
Mulugeta Gebregziabher,
Ralph H. Johnson		 source 18
SESUG 2014 Trash to Treasures: Salvaging Variables of Extremely Low Coverage for Modeling Alec Zhixiao Lin source 5
MWSUG 2014 Reducing the Bias: Practical Application of Propensity Score Matching in Healthcare Program Evaluation Amber Schmitz,
Jessica Navratil-Strawn,
Stephen Hartley,
Ronald J. Ozminkowski		 7
MWSUG 2014 Using Arrays to Quickly Perform Fuzzy Merge Look-ups: Case Studies in Efficiency Arthur L. Carpenter 14
WUSS 2014 Using and Understanding LSMEANS and LSMESTIMATE David J. Pasta source 20
WUSS 2014 Using PROC FACTOR for variable inclusion in PROC SURVEYLOGISTIC: A Case Study Deanna Naomi Schreiber-Gregory source 35
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 Common and Comparative Incidence Indicators of Adverse Events for Well-Defined Study Pools John R. Gerlach source 7
PharmaSUG 2014 Estimating Medication Adherence Using a Patient-Mix Adjustment Method R. Scott Leslie source 9
PharmaSUG 2014 Risk-Based Approach to Identifying and Selecting Clinical Sites for Sponsor's Preparation for FDA/EMA Inspection Xiangchen (Bob) Cui source 21
PharmaSUG 2014 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 19
PharmaSUG 2014 The Association of Morbid Obesity with Mortality and Coronary Revascularization among Patients with Acute Myocardial Infarction Ashwini S Erande,
Shaista Malik		 source 11
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 Analyzing U.S. Healthcare Cost and Use with SAS Paul Gorrell source 17
SGF 2014 How SAS BI May Help You to Optimize Business Processes Plinio Faria source 3
WUSS 2013 Estimating Medication Adherence Using a Patient-Mix Adjustment Method R. Scott Leslie source 9
WUSS 2013 How to Compute Custom Hazard Ratio in SAS (V8.2&V9.2) PHREG Procedure Liguo Cheng,
Yu Li		 source 8
WUSS 2013 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 24
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
SESUG 2013 Comparing SAS PROC MI and IVEware Callable Software Bruno Vizcarra,
Amang Sukasih		 source 11
PHUSE EU 2013 Bayesian Statistics: Concept and Bayesian Capabilities in SAS Mark Janssens source
presentation source		 15
PHUSE EU 2013 Extensions of Cox Model for Non-Proportional Hazards Purpose Jadwiga Borucka source
presentation source		 10
MWSUG 2013 A Concise Display of Multiple Response Items Patrick Thornton 7
MWSUG 2013 Adventures in Path Analysis and Preparatory Analysis Brandy R. Sinco,
Phillip L. Chapman		 19
MWSUG 2013 Analyzing Categorical Variables from Complex Survey Data Using PROC SURVEYFREQ Taylor Lewis 20
MWSUG 2013 Analyzing Continuous Variables from Complex Survey Data Using PROC SURVEYMEANS Taylor Lewis 14
MWSUG 2013 Residential Energy Efficiency and the Principal-Agent Problem Ryan Anderson 11
NESUG 2013 Analyzing Continuous Variables from Complex Survey Data Using PROC SURVEYMEANS Taylor Lewis 15
NESUG 2013 Does the cat have your tongue? Using VNAME and CATX to get SAS to speak to you. Gayle K. Springer 4
NESUG 2013 The Smarts of Using Dummies Patricia Meholick 4
PharmaSUG 2013 SAS Logic Coding Made Easy - Revisit User-defined Function Songtao Jiang source 6
SGF 2013 A Concise Display of Multiple Response Items Patrick Thornton source 7
SGF 2013 Being Continuously Discrete (or Discretely Continuous): Understanding Models with Continuous and Discrete Predictors and Testing Associated Hypotheses David J. Pasta source 19
SGF 2013 Build Prognostic Nomograms for Risk Assessment Using SAS Dongsheng Yang source
pdf		 7
SGF 2013 Considerations and Techniques for Analyzing Domains of Complex Survey Data Taylor Lewis source 12
SGF 2013 Cox Proportional Hazard Model Evaluation in One Shot Polina Kukhareva source 11
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 Short-Term Costs of Smoking during Pregnancy: A Geometric Multidimensional Approach Violeta Balinskaite source 11
NESUG 2012 Efficiently Screening Predictor Variables for Logistic Models Steven Raimi,
Bruce S. Lund		 13
NESUG 2012 Generating Estimates for U.S. Healthcare Costs and Use Paul Gorrell 18
NESUG 2012 Yes, No, Maybe So: Tips and Tricks for Using 0/1 Binary Variables Laurie Hamilton 6
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 Fitting Bayesian hierarchical multinomial logit models in PROC MCMC Fitting Bayesian source 8
MWSUG 2012 Are Sales Figures in Line With Expectations? Using PROC ARIMA in SAS to Forecast Company Revenue Saveth Ho,
Brian Van Dorn		 10
MWSUG 2012 Modeling Complex Survey Data Taylor Lewis 16
MWSUG 2012 Random assignment of proxy event dates to unexposed individuals in observational studies: An automated technique using SAS Raymond Harvey,
Dana Drzayich Jankus,
David Mosley		 8
WUSS 2012 A Concise Display of Multiple Response Items Patrick Thornton source 5
WUSS 2012 Being Continuously Discrete (or Discretely Continuous): Understanding Models with Continuous and Discrete Predictors and Testing Associated Hypotheses David J. Pasta source 19
WUSS 2012 Best Practices: Use Coding Shortcuts to Avoid Typsos and Add Flexibility Mary F. O. Rosenbloom,
Edwards Lifesciences,
Kirk Paul Lafler		 source 7
WUSS 2012 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Arthur L. Carpenter,
Christopher F. Ake		 source 7
WUSS 2012 The Steps to Follow in a Multiple Regression Analysis Theresa H. Ngo source 12
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 Algorithm to Compare the Slopes (Regression Coefficients) between the subgroups in Simple/Multiple Regression using PROC REG Sandeep Sawant source 3
PharmaSUG 2012 Common Misunderstandings about ADaM Implementation Nate Freimark,
Susan J. Kenny,
Jack Shostak,
John K. Troxell		 source 10
PharmaSUG 2012 Is Your Failed Macro Due To Misjudged "Timing"? Arthur X. Li source 22
SGF 2012 Black Box PROCs: PROC LOGISTIC Discovered Isabel H. Perry 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 Is Your Failed Macro Due To Misjudged "Timing"? Arthur X. Li source 22
SGF 2012 Knowledge (of Your Missing Data) Is Power: Handling Missing Values in Your SAS Data Set Theresa Schwartz,
Rachel Zeig-Owens		 source 8
SGF 2012 Using SAS to Extend Logistic Regression Dachao Liu source 8
SGF 2012 Your Survival Guide to Using Time-Dependent Covariates Melissa E. Bagnell,
Teresa M. Powell		 source 10
SCSUG 2011 Advice to Health Services Researchers: Be Cautious Using the "Where" Statement in SAS Programs for Nationally Representative Complex Survey Data Hemalkumar Mehta source 10
SESUG 2011 Tailoring Logistic Regression Model Analyses with the ODDSRATIO Statement in PROC LOGISTIC Taylor Lewis source 8
SESUG 2011 Time Series Analysis: Separating Overlapping Events M. Scott Elliott source 10
WUSS 2011 A SAS Macro Program to Calculate Medication Adherence Rate for Single and Multiple Medication Use Hao Chu source 11
WUSS 2011 Why Dummy Variable Makes You SMART, and How to Do it SEXY Brian Sun source 12
MWSUG 2011 Does Lawrence Smoking Ban Impact Kansas Liquor Sales? Lijia Mo 12
MWSUG 2011 Using SAS to Extend Logistic Regression Dachao Liu 8
PharmaSUG 2011 Adjusting Analyses of Survey Results using a Predicted Probability of Response Rob Gately source 12
SGF 2011 After SAS Performs the Surgery, Excel Applies the Make-Up: Making Healthcare Data Look Pretty Andrea Scott source 5
SGF 2011 Bivariate Count Data Regression Models: A SAS Macro Program Nan-Ting Chou,
David Steenhard		 source 10
SGF 2011 Multivariate Data Displays for Evaluating Clusters Robert G. Moore source 9
SGF 2011 PROC PANEL Vs. A FAST ALGORITHM FOR ESTIMATION OF UNBALANCED PANEL DATA MODELS WITH TWO FIXED EFFECTS Alan Ricardo da Silva,
Mariana Fernandes Teixeira,
Thaís Helena Fernandes Teixeira		 source 12
SGF 2011 Rasch Model and Beyond with PROC GLIMMIX: A Better Choice Teck Kiang Tan,
Wenshu Luo,
David Hogan		 source 12
SGF 2011 Using PROC LOGISTIC to Estimate the Rasch Model Tianshu Pan,
Yumin Chen		 source 7
NESUG 2010 After SAS Performs the Surgery, Excel Applies the Make-Up: Making Healthcare Data Look Pretty Andrea E. Scott 5
WUSS 2010 An Introduction to Statistical Tests for the SAS Programmer Sara Beck source 10
WUSS 2010 Dummy Coding for Dummies Kathryn Martin source 10
WUSS 2010 Expanding Treatment Start and Stop Dates into a Daily Account of Treatment Exposure Faith Beery source 4
WUSS 2010 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Christopher F. Ake source 7
PHUSE EU 2010 Assesment of Cox Proportional Hazard Model Adequacy Using PROC PHREG and PROC GPLOT Jadwiga Borucka source
presentation source		 13
PHUSE EU 2010 Updating an MS SQL database from SAS Jim Groeneveld source
presentation source		 8
MWSUG 2010 Multivariate Data Displays for Evaluating Clusters Robert G. Moore 9
MWSUG 2010 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Christopher F. Ake,
Arthur L. Carpenter		 6
PharmaSUG 2010 An Overview of the CLASS, CONTRAST, and HAZARDRATIO Statements in the SAS 9.2 PHREG Procedure Paul T. Savarese,
Michael J. Patetta		 23
SGF 2010 Addressable Minds for Understanding the Perception of Marketing Messages Howard J. Moskowitz,
Dana Rafiee		 source 5
SGF 2010 An Overview of the CLASS, CONTRAST, and HAZARDRATIO Statements in the SAS 9.2 PHREG Procedure Paul T. Savarese,
Michael J. Patetta		 source 23
SGF 2010 Building Latent Growth Models Using PROC CALIS: A Structural Equation Modeling Approach Teck Kiang Tan,
Trivina Kang,
David Hogan		 source 19
SGF 2010 Dancing the Sample-Size Limbo with Mixed Models: How Low Can You Go? Bethany A. Bell,
Grant B. Morgan,
Jason A. Schoeneberger,
Brandon L. Loudermilk,
Jeffrey D. Kromrey,
John M. Ferron		 source 11
SGF 2010 Principles of Proper Inferences from Complex Survey Data Taylor Lewis source 9
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 Resolving Value Differences among Duplicates, A Rule-based Approach David H. Abbott,
L. Douglas Melton,
Steven C. Grambow,
George L. Jackson		 source 11
PNWSUG 2009 Building Multiple Linear Regression Models - Food for Thought Colleen E. McGahan,
Mei Chong		 11
NESUG 2009 Ad Hoc Data Preparation for Analysis Using SAS Enterprise Guide I-Kong B. Fu 11
NESUG 2009 What Drives Physicians to Use A Particular Product when Prescribing Marc States 5
WUSS 2009 Computing Hazard Ratios and Confidence Intervals for a Two-Factor Cox Model with Interaction Terms Marla J. Husnik,
Jing Wang		 source 10
WUSS 2009 The SURVEYFREQ Procedure in SAS 9.2: Avoiding FREQuent Mistakes When Analyzing Survey Data Kathryn Martin source 6
WUSS 2009 Trend Analyses of Health Indicator and Risky Health Behavior Variables in Nevada George C. J. Fernandez,
Moses Anabila		 source 9
WUSS 2009 Using PROC REPORT to Cross-Tabulate Multiple Response Items Patrick Thornton source 5
PharmaSUG 2009 Computing Hazard Ratios and Confidence Intervals for a Two-Factor Cox Model with Interaction Terms Jing Wang,
Fred Hutchinson Cancer		 10
PharmaSUG 2009 PROC REPORT: Compute Block Basics - Part II Practicum Arthur L. Carpenter 18
SGF 2009 %QLS SAS Macro: A SAS Macro for Analysis of Longitudinal Data Using Quasi-Least Squares Hanjoo Kim,
Justine Shults		 source 12
SGF 2009 Ad Hoc Data Preparation for Analysis Using SAS Enterprise Guide I-Kong B. Fu,
Wayne Thompson,
Mike Porter		 source 11
SGF 2009 Automate Dummy Variable Created from Large Longitudinal RDBMS Tables Melissa Skanderson,
Xiaoqiang Wang		 source 10
SGF 2009 Learning When To Be Discrete: Continuous vs. Categorical Predictors David J. Pasta source 10
SGF 2009 PROC SQL and SAS Macro: Beyond the Basics Louie Huang source 8
WUSS 2008 Calculating Medication Compliance, Adherence, and Persistence in Administrative Pharmacy Claims Databases R. Scott Leslie source 8
WUSS 2008 Demystifying Popular SAS Procedures. Lisa Smith source 17
WUSS 2008 Learning When To Be Discrete: Continuous vs. Categorical Predictors David J. Pasta source 6
WUSS 2008 PROC REPORT: Compute Block Basics - Part II Practicum Arthur L. Carpenter source 18
SESUG 2008 Marketing Mix Modeling: Techniques and Challenges Patralekha Bhattacharya source 6
MWSUG 2008 How to Generate Dynamical and Flexible Codes in Clinical Trial Xianming (Steve) Zheng 3
MWSUG 2008 PROC REPORT: Compute Block Basics - Part II Practicum Arthur L. Carpenter 18
MWSUG 2008 Scheduling College Classes Using Operations Research Techniques Barry E. King,
Terri Friel		 4
PNWSUG 2008 Applied Aspects of Using Multiple Imputation in Clinical Data Christina Gullion 7
NESUG 2008 An Introduction to the SAS Survey Analysis PROCs Xiuhua Chen,
Paul Gorrell		 17
NESUG 2008 Dummy Variables and the Relationship of Deaf and Hearing Growth Using SAS/GRAPH Richard Dirmyer,
Sara Schley		 6
NESUG 2008 Proc Logistic Plus: The Power Of Variable Transformations Krishna Mehta,
Nitin K. Jain,
Varun Aggarwal		 4
PharmaSUG 2008 Calculating Medication Compliance, Adherence, and Persistence in Administrative Pharmacy Claims Databases R. Scott Leslie 8
PharmaSUG 2008 Getting the MAX Out of PROC MEANS R. Scott Leslie 9
PharmaSUG 2008 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 14
PharmaSUG 2008 TPHREG and GLMSELECT: Using the CLASS statement in SAS 9.1 Laura E. Archer 11
SGF 2008 BFF: Binary Flags Forever (Or, Boole's Paradise) David J. Pasta,
Eric P. Elkin		 source 9
SGF 2008 Computational Algorithm for Estimation of Panel Data Models with Two Fixed Effects Alan Ricardo da Silva,
Patrick F. Alves		 source 10
SGF 2008 Convergence Failures in Logistic Regression Paul D. Allison source 11
SGF 2008 PROC REPORT: Compute Block Basics - Part II Practicum Arthur L. Carpenter source 18
SGF 2008 Using SAS/OR to Improve Service Completion at a Fortune 100 Company Chuck Kelly,
Jinxin Yi,
Philip Easterling		 source 9
SGF 2008 Using Text Mining to Classify Requests and Prepare Semiautomatic Answers Wolfgang Himmel,
Ulrich Reincke,
Hans W. Michelmann		 source 8
NESUG 2007 A Lazy Programmer Case Study: Dynamic Macro Code to Deal with Changing Number of Variables over Time Suzanne M. Dorinski 14
NESUG 2007 Offset Techniques in Predictive Modeling for Insurance Matthew J. Flynn,
Jun Yan		 9
NESUG 2007 PROC LOGISTIC: The Logistics Behind Interpreting Categorical Variable Effects Taylor Lewis 7
MWSUG 2007 Using Data Mining Techniques to Predict Student Development and Retention Morgan C. Wang 59
WUSS 2007 BFF: Binary Flags Forever (Or, Boole's Paradise) David J. Pasta,
Eric P. Elkin		 source 8
WUSS 2007 Getting the MAX Out of PROC MEANS R. Scott Leslie source 9
SGF 2007 Attitudes toward Immigrants and Immigration in the United States: Toward a Structural Approach Michael J. Sobczak source 12
SGF 2007 Using Arrays to Calculate Medication Utilization R. Scott Leslie source 5
PHUSE EU 2006 The Hazards of Modelling Proportional Hazards in SAS Erika Daly,
Farzana Sufi		 source 4
PHUSE EU 2006 Using CDISC Models for the Analysis of Safety Data Susan J. Kenny,
Edward D. Helton		 source 10
SESUG 2006 Creating RTF Tables with Univariate Analyses of Multiply Imputed Data Marie Gantz source 14
SESUG 2006 Detection and Quantification of Asymmetric Peaks Carry W. Croghan,
Ronald W. Williams		 source 13
WUSS 2006 Detecting Structural Change Using SAS ETS Procedures Archie J. Calise,
Joseph E. Earley		 source 7
WUSS 2006 Dynamic Code Creation Using Call Symput and the SAS Macro Language Mark Gustafson source 4
WUSS 2006 Hierarchical Logistic Regression Modeling with SAS GLIMMIX Jian Dai,
Zhongmin Li,
David Rocke		 source 9
WUSS 2006 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 source 14
WUSS 2006 Using Arrays to Calculate Medication Utilization Scott P. Leslie source 4
NESUG 2006 Custom Reports with Proc SQL: A Real-Life Example Electra Small 11
NESUG 2006 Survey Logistic Regression: Some SAS-SUDAAN Comparisons Xiuhua Chen 7
NESUG 2006 The National Occupational Respiratory Mortality System: Deployment of SAS/IntrNet Application Dispatcher Queries Using SAS Component Language (SCL) Macros John Wood 10
SUGI 2006 Fixed Effects Regression Methods in SAS Paul D. Allison source 20
SUGI 2006 Introduction to SAS Enterprise Guide 4.1 for Statistical Analysis Charles B. Hallahan,
Linda P. Atkinson		 source 22
PNWSUG 2005 Assessing Within- and Between-Subject Effects with Correlated Clinical Data Marla J. Husnik,
Eugene Huang,
Grant Colfax		 10
SESUG 2005 Using ODS to Perform Simulations on Statistics from SAS Procedures Andrew A. Kramer source 4
PHUSE EU 2005 Empty lines in SAS tables (list output and ODS RTF) produced by Proc Tabulate Twan van Berkel source 15
WUSS 2005 A Comparison of Two Procedures to Fit Multi-Level Data: PROC GLM versus PROC MIXED MaryAnne DePesquo Hope source 7
WUSS 2005 A Journey to Ellis Island: Profiling Historical Immigration Using SAS and JMP Time Series Methods Archie J. Calise,
Joseph E. Earley		 source 6
NESUG 2005 %htmlForm: An HTML Form and SAS/IntrNet Code Generator Don Boudreaux 8
NESUG 2005 PROC SURVEYSELECT: A Simply Serpentine Solution for Complex Sample Designs Louise S. Hadden 8
PharmaSUG 2005 Assessing Within- and Between-Subject Effects with Correlated Clinical Data Marla J. Husnik,
Eugene Huang,
Grant Colfax		 10
PharmaSUG 2005 Calculating Posterior Probability of the Maximum Contrast Using PROC IML Akira Wakana,
Isao Yoshimura,
Chikuma Hamada		 6
PharmaSUG 2005 Programming Tips and Examples for Your Toolkit, II John Morrill 12
SUGI 2005 %htmlForm: An HTML Form and SAS/IntrNet Code Generator Don Boudreaux,
Keith Cranford		 source 10
SUGI 2005 A Comparison of Two Procedures to Fit Multi-Level Data: PROC GLM versus PROC MIXED Erin D. Shannon,
MaryAnne DePesquo Hope		 source 7
SUGI 2005 Analyzing Incomplete Binary Repeated Measures Data Using SAS Bin Yang source 8
SUGI 2005 Imputation of Categorical Variables with PROC MI Paul D. Allison source 14
SUGI 2005 Parameterizing Models to Test the Hypotheses You Want: Coding Indicator Variables and Modified Continuous Variables David J. Pasta source 20
SUGI 2005 Rounding after Multiple Imputation with Non-Binary Categorical Covariates Christopher F. Ake source 11
SUGI 2005 Teaching Data Mining in a University Environment David A. Dickey source 11
NESUG 2004 A Regression Primer with a Touch of SAS Vincent Maffei 14
NESUG 2004 An Animated Guide: Proc UCM Russell M. Lavery 23
NESUG 2004 The Piecewise Regression Model as a Response Modeling Tool Eugene Brusilovskiy 10
SESUG 2004 Case Studies in Time Series David A. Dickey source 10
SESUG 2004 Case Studies in Time Series: Periodic Behavior and Related Issues Periodic Behavior,
Related Issues,
David A. Dickey		 source 16
WUSS 2004 A SAS Macro to Calculate Standardized Cronbach's alpha using Upper Bound phi Coefficient for Dichotomous Items Weimin (Wiliiam) Sun,
Alan W. Stacy,
Huiyan Ma,
Chih-Ping Chou		 source 10
WUSS 2004 Kaplan Meier And Cox Proportional Hazards Modeling: Hands On Survival Analysis Tyler C. Smith,
Besa Smith		 source 14
WUSS 2004 Parameterizing Models to Test the Hypotheses You Want: Coding Indicator Variables and Modified Continuous Variables David J. Pasta source 12
WUSS 2004 Using Classification Tree Outcomes to Enhance Logistic Regression Models Tim Millard source 4
MWSUG 2004 Presenting the Binary Logit Model Effectively using SAS Hun Myoung Park 11
PharmaSUG 2004 %Cox_PH - An enhancement of SAS PROC PHREG Hong (Lily) Zhang,
Robin Mogg		 8
PharmaSUG 2004 Frequency Generation Using Indicator Variable Jinshi Zhou 3
SUGI 2004 Case Studies in Time Series II: Periodic Behavior and Related Topics David A. Dickey source 17
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 The Power of CALL SYMPUT - DATA Step Interface by Examples Yunchao (Susan) Tian source 6
SUGI 2004 Using SAS Procedures to Make Sense of a Complex Food Store Survey Jeffrey M. Gossett,
Pippa M. Simpson,
Renée A. Hall,
Carol Connell,
Kathy Yadrick,
Margaret L. Bogle		 source 8
WUSS 2003 Efficient Statistical Programming? Let's Be Creative With Those SAS Steps Keiko I. Powers source 7
SEUGI 2003 Data Mining for Predicting Customer Satisfaction [presentation] Francesco Vivarelli source 35
PharmaSUG 2003 An Application of Dummy Variable and Placeholder Macro Parameter in Programming for Clinical Trial Data Quality Monitoring Sijian Zhang,
Mark Carpenter		 4
SUGI 2003 Case Studies in Time Series David A. Dickey source 9
SUGI 2003 Cutpoint Determination Methods in Survival Analysis Using SAS Jayawant N. Mandrekar,
Sumithra J. Mandrekar,
Stephen S. Cha		 source 5
SUGI 2003 If Only 'Page 1 of 1000' Jiang Jin,
Ye Jin,
Diane Wang		 source 2
SUGI 2003 Usage Statistics for Your Web Site: Leveraging the Flexibility of SAS and Webhound Lynn Rohrs,
Carol L. Markowitz		 source 4
WUSS 2002 Survival Analysis with PHREG: Using MI and MIANALYZE to Accommodate Missing Data Arthur L. Carpenter source 6
SUGI 2002 Stop Madly Merging: PROC PRINT to the Rescue! Catherine A. Lindsey source 2
VIEWS 2001 Data Mining and Statistical Models in Marketing Campaigns of BT Retail Francesco Vivarelli,
Martyn Johnson		 9
WUSS 2001 Factor Analysis of Categorical Data in SAS Lei Han,
Torsten B. Neilands,
Margaret Docini		 source 6
SEUGI 2001 Customer Profiling System in Telecom Italia Mobile [presentation] Claudio Bocci,
Gianni Santerini		 source 44
SUGI 2001 Efficient Statistical Programming? - Let SAS Do the Work Keiko I. Powers source 4
SUGI 2001 Using SAS Enterprise Miner for Forecasting Kattamuri S. Sarma source 6
NESUG 2000 Ad hoc Validation of Multivariate Regression Using PROC FACTOR with BY Groups Susan B. Garavaglia,
Asha Sharma,
Stephen Thompson		 10
NESUG 2000 Applying Survival Analysis Techniques to Loan Terminations for HUD's Reverse Mortgage Insurance Program - HECM Ming H. Chow 8
NESUG 2000 Risk-Adjustment of Cesarean Section Rates as Part of Clinical Quality Improvement: Two Applications of PROC LOGISTIC Guy Pascale 3
WUSS 2000 Analyzing Multiple Health Outcomes Using the SAS System's PROC CATMOD Besa Smith source 5
SEUGI 2000 The Art of Building Predictive Scorecards [presentation] Duncan Hale source 15
SEUGI 2000 Using Enterprise Miner to explore pattern and process in biodiversity surveillance data for Great Britain [presentation] John W. Watkins,
Simon M. Smart		 source 11
SUGI 2000 Correcting for Missing Discrete Responses in Business Surveys Lawrence C. Marsh source 10
SCSUG 1999 Normalization of Peak Demand for an Electric Utility Using PROC Model J. Mark Harris,
Jeff Brown,
Mark Gilbert		 7
WUSS 1999 Application of Poisson Model in Non-Market Valuation: Cave Rock Climbing Data Analysis Using SAS Macros Yuanfang Lin source 5
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 The missing link: PROC GENMOD Margaret Ann Goetz 3
MWSUG 1999 REG, GLM, ANOVA: Which One? Why? How? Linda M. Quinn 6
SEUGI 1999 Segmentation of the Slovenian retail bank market Anuska Ferligoj,
Igor Horvat,
Joze Rovan		 source 14
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
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
NESUG 1998 A Smart Guide to Dummy Variables: Four Applications and a Macro Susan B. Garavaglia 10
NESUG 1998 Multivariate Discrimination of Categorical Data Using the SAS System Jeffrey S. Butler 8
SESUG 1998 Programmatic Generation of Table Specifications Using the SAS Macro Facility Weimin "WilHam" Sun,
Caroline Stewart		 10
PNWSUG 1998 Stepwise Anything: Reading in Regression Procedure Output for Further Testing Ted Clay,
Linda Remy		 10
SEUGI 1998 Defining groups of "typical investors": hierachical clustering for qualitative data Raffaela Piccarretta source 7
PNWSUG 1997 Using SAS to Determine the Revenue Impacts from Emerging Products Mark E. Thompson 8
WUSS 1997 The Sums of Squares Are All the Same - How Can the LSMEANS Be So Different? Lori Potter,
David J. Pasta		 source 6
NESUG 1997 A Multivariate Analysis of Airline Service Quality Vasanthakumar Bhat 7
NESUG 1997 Macro to Determine the Significance of the Main Effects of Two Categorical Factors for the PHREG Procedure Robert J. Gallop 9
NESUG 1997 Saving Trees and Time - A SAS Macro for Testing and Reporting the Association Between Multiple Candidate Variables and a Single Binary Variable Christianna S. Williams 9
NESUG 1997 Using Logistic Regression to Test for Interaction in the Presence of Zero Cells Lisa Suchower 10
MWSUG 1997 An Introduction to Survival Analysis Linda M. Quinn 12
SESUG 1997 Learning About Your Data: Tips and Techniques for Looking at Large Files Sandra D. Schlotzhauer 10
SESUG 1997 Modeling Higher Education Processes with Multiple Logistic Regression: SAS Examples Daniel Teodorescu 5
SEUGI 1997 The GDP forecast for the Slovak Republic Josef Chajdiak source 5
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 Introduction to Building a Linear Regression Model Leslie A. Christensen source 7
SUGI 1997 Learning About Your Data: Tips and Techniques for Looking at Large Files Sandra D. Schlotzhauer,
Bob Anschuetz		 source
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
NESUG 1996 Forecasting Business Failures Using a Poisson Regression Model Vasanthakumar Bhat 5
NESUG 1996 Poor Mans's Missing Value Recovery Methods Leonardo Auslender 10
PNWSUG 1996 Net Impact for Program Evaluation Modeling and SAS Programming Boqing Wang 18
SEUGI 1996 A hedonic price index of the house price statistics Hannu Lehtimaki,
Markku Verkkoniemi		 source 5
SEUGI 1996 Teaching about executive information systems through the medium of a computer-based ... Teaching about Executive Information Systems through the medium of a computer-based tutorial developed using the D. Glass,
K. Smyth,
R. N. Wilson		 source 5
SUGI 1996 Allocation of Military Engines to Fighter Aircraft: A SAS/OR Application Paul R. Sheridan,
Timothy F. Ousley		 source 4
MWSUG 1995 Data Management and Manipulation: Examples for Normalized Databases and Spreadsheets Marlene Goormastic,
Shelly Sapp		 6
NESUG 1995 Capitalizing Names With PROC FSVIEW Formulas Alan C. Wilson 2
SESUG 1995 New Age Marketing: Past Life Regression versus Logistic Regression C. Olivia Parr Rud 9
SEUGI 1995 A Survival Analysis on Critical Components of Nuclear Power Plants Valerie Durbec source 9
SEUGI 1995 Model Building for the Cox Proportional Hazards Model with the SAS System Richard Vonk source 12
SEUGI 1995 Using the SAS System in the Development of Frameworks for the Analysis of Accelerated Life Testing Experiments Alan John Watkins source 10
SUGI 1995 A Comparative Evaluation of Selected Statistical Software for Computing Various Categorical Analyses Nancy McDermott,
Cynthia White		 source 6
SUGI 1995 Karnaugh Maps, Interaction Effects and Creating Composite Dummy Variables for Regression Analysis in SAS Software Lawrence C. Marsh,
Karin Wells		 source 10
SUGI 1995 Matching Observations on Both Continuous and Categorical Variables: A Simple Application of the FASTCLUS Procedure Manuel Lombardero source 6
SUGI 1995 SAS Macros Useful in Imputing Missing Survey Data Barbara Lepidus Carlson,
Brenda Cox,
Linda Bandeh		 source 6
NESUG 1994 How to Survive PROC LIFETEST Peter P. Gaccione 10
NESUG 1994 Using of the PHREG Procedure in a Matched Case-Control Analysis Karin LaPann 4
MWSUG 1994 Transforming Data in SAS: Restructuring Data Sets, Creating Look-Up Tables, and Forming Person-Year Recores for Event History Analysis in SAS Lawrence C. Marsh,
Karin L. Wells		 7
SESUG 1994 Managing Overkill: How to Use SAS PROC CA TMOD to Estimate Odds Ratios Sigurd W. Hermansen 5
SEUGI 1994 Practical Aspects of Modelling Techniques in Logistic Regression Procedures of the SAS System Josef Hogel source 6
SEUGI 1994 Statistical Decision Support - Expert System for Examining Decaying and Aging Manpower Displacements Yechezkel Kling source 8
SUGI 1994 Using SAS Software to Check Assumptions for Analysis of Covariance, Including Repeated Measures Designs Richard P. Steiner,
N. Margaret Wineman		 source 5
SCSUG 1993 An Algorithm for Creating Multiple Response Variables Paul E. Pope 4
SEUGI 1993 A New Look at the Facilities in PROC MIXED James L. Roger source 12
SEUGI 1993 Analysing Multiple Response Survey Data: An Overview of Strategies and Methods Paul E. Pope source 8
SUGI 1993 Forecasting Methods Mark Little source 13
SUGI 1993 SAS Software to Fit the Generalized Linear Model Gordon Johnston source 8
SUGI 1992 Testing the Rationality of Panel Data Forecasts Using a GMM Estimator Richard H. Cohen source 6
SUGI 1991 A Program for Outputting P Values and Confidence Intervals in an Analysis of Variance without the Use of the GLM or PR/NTTO Procedures Susan Foley Anton,
Joseph J. Andary		 source 6
SUGI 1991 A SAS Macro for the Robust Analysis of Repeated Measures Data Joe DiGennaro,
William J. Huster		 source 6
SEUGI 1990 Handling of Open-Ended Questions Using the SAS System B. M. Christensen source 12
SUGI 1990 Alternative Methods of Estimating Piecewise Linear and Higher Order Regression Models Using SAS Software Lawrence C. Marsh,
Manjula Maudgal,
Jaishankar Raman		 source 5
SUGI 1990 Tips and Tricks on Rearranging and Manipulating Data and Datasets for Efficient Data Processing and Output Generation Donald D.M. Tong,
Thomas E. Schneider,
Pan-Yu Lai		 source 4
SUGI 1990 Unbiased Estimators of Shrinkage for Chi-Square Based Measures of Association Richard F. Haase source 6
NESUG 1989 Efficient Processing of Large Datasets Howard Levine 10
SUGI 1989 Confounding of Effects in Equal n Designs: Factorial ANCOVA Scott E. Maxwell,
Harold D. Delaney		 source 6
SUGI 1989 Unbalanced Factorial Data and Hypothesis Testing Robert E. Johnson source
SUGI 1988 Distribution System Design with SAS/OR Software and the Matrix Generator Narendra Gangoli,
Richard T. Jenkins		 source 5
SUGI 1988 PROC DICHOT: A SAS Procedure for Creating Dichotomous Variables Paul Lambert source 6
SUGI 1988 The Influence of Economic Variables on Seminary Participation: A SAS/ETS Application Archie J. Calise,
Joseph E. Earley		 source 5
SUGI 1988 Using SAS/STAT Software's REG Procedure to Develop Sales Tax Audit Selection Models Kirk L. Johnson,
Richard W. Kulp		 source 6
SEUGI 1987 A Statistical Look at Risk Factors for Coronary Heart Disease David J. Shannon source 15
SEUGI 1987 Tutorial on PROC REG Gerhard Held source 39
SUGI 1986 Choosing Dummy Variables for a General Linear Model - Intelligently Stephan Arndt source 3
SUGI 1986 Using PROC NLIN for Time Series Prediction Wayne Fuller source 6
SUGI 1986 Using SAS/FSP Software and the Macro Facility to Create a Generic Report Writer J. Page Miller source 6
SEUGI 1985 Test Procedures for Model Selection and Model Validation Wil Velding source 23
SUGI 1985 A Time Series Approach to Modeling Daily Peak Electricity Demands Michael F. Jacob source 6
SUGI 1985 Conditional Logistic Regression in SAS Software Gregg A. Drube,
Dale F. Kraemer,
Stephanie Van Lier		 source 7
SUGI 1985 Descriptive Statistics: Using Indicator Variables Tamara R. Fischell,
Juliana M. Ma		 source 5
SUGI 1984 Computing for Unbalanced Repeated Measures Experiments Kenneth N. Berk source 7
SUGI 1984 PROC SUMMARY as the Basis for Efficient Analyses of Large Files Carol Leininger,
Juliana M. Ma		 source 4
SUGI 1983 Automation of Estimation and Sampling Error Computations Using PROCs NASSTIM and NASSVAR Greg Binzer,
David Morganstein		 source 6
SUGI 1983 On Estimating Spline Regressions Lawrence C. Marsh source
SUGI 1983 PROC MCSTRAT Susanne L. Daood,
James M. Naessens,
Kenneth P. Offord		 source 8
SUGI 1982 Pesticides and SAS: Data Processing for EPA Monitoring Networks Dennis W. Herrin,
David C. Moazed,
David L. Myers,
Jeanne R. Pagano,
Deborah H. Whitehorne		 source 6
SUGI 1982 PROC TETCORR: Correlations between Categorical and Continuous Variables. J. Philip Miller,
Michael A. Province,
Reimut Wette		 source 4
SUGI 1981 Parameter Estimation, Hypothesis Testing and Coding Chip Kaliher source

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