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416 papers and 1 presentation; 373 titles.

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Conference × Year × Title × Authors Extras Pages
MWSUG 2025 How to Modify SAS 9 Programs to Run in SAS Viya Danny R. Modlin source 40
PHUSE US 2025 Comparing Confidence Intervals for a Difference in Adjusted Predicted Probabilities Cralen Davis,
Trevano Dean		 source
presentation source		 5
WUSS 2024 Integration of an R Language Function Within SAS to Perform Conditional Multivariate Saddlepoint Approximation Christopher Johnson source 8
PharmaSUG 2024 Multiple Logistic Regression Analysis using Backward Selection Process on Objective Response Data with SAS Girish Kankipati,
Jai Deep Mittapalli		 source 9
WUSS 2023 Leveraging Logistic Regression and Bootstrap Sampling to Assess a Decision Threshold in Classification Predictive Analytics Colleen E. McGahan source 15
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
PharmaSUG 2022 Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software Matthew T. Slaughter,
Isaiah Lankham		 source 22
SESUG 2021 Screening, Binning, Transforming Predictors for a Generalized Logit Model Bruce S. Lund source 17
SGF 2021 A Framework for Simple and Efficient Bootstrap Validation in SAS, with Examples Isaiah Lankham,
Matthew T. Slaughter		 source 22
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
SGF 2020 Analyzing Non-normal Data: Application to Missing Data Problems Niloofar Ramezani source 10
SGF 2020 Best Practices for Converting SAS Code to Leverage CAS Steven Sober,
Brian Kinnebrew		 source 11
SGF 2020 Propensity Score Matching with Survey Data Bocheng Jing source 14
SGF 2020 Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software Isaiah Lankham,
Matthew T. Slaughter		 source
github		 15
SGF 2020 Use SAS to Model Non-Linearity Xiaoting Wu,
Donald S. Likosky		 source 12
PharmaSUG 2020 %MVMODELS: a Macro for Survival and Logistic Analysis Jeffrey P. Meyers source 23
PHUSE US 2020 Penalized Likelihood Logistic Regression for Sparse Data Using Data Priors Martin Dörr source
presentation source		 5
SESUG 2019 Advanced Statistical Modeling within Machine Learning and Big Data Analytics Niloofar Ramezani,
George Mason		 source 16
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 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
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
WUSS 2019 Automating a Summary Report of PSM Model and Match Results Desiree Hall source 9
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 Integrating Case Studies in a Health Analytics Curriculum Besa Smith,
Tyler C. Smith		 source 21
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 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 Transforming Ordinal Predictors to Numeric For Binary Logistic Models Bruce S. Lund source 10
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
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 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
PharmaSUG 2018 Summary Table for Displaying Results of a Logistic Regression Analysis Lori S. Parsons source 9
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 Claim Risk Scoring Using Survival Analysis Framework and Machine Learning with Random Forest Yuriy Chechulin source 15
SGF 2018 Evaluation of Different Approaches to Reject Inference: a case study in Credit Risk Sergiu Luca source 9
SGF 2018 Insights into Using the GLIMMIX Procedure to Model Categorical Outcomes with Random Effects Kathleen Kiernan source 23
SGF 2018 Power Analysis for Generalized Linear Models Using the New CUSTOM Statement in PROC POWER John M. Castelloe source 17
SGF 2018 The Function Selection Procedure Bruce S. Lund source 11
SESUG 2017 Applying Logistics Regression to Forecast Annual Organizational Retirements Alan Dunham source 12
SESUG 2017 Binning of Predictors for the Cumulative Logit Model Bruce S. Lund source 20
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
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 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 Is My Model the Best? Methods for Exploring Model Fit Deanna Naomi Schreiber-Gregory 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 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 Analyzing Residuals in a PROC SURVEYLOGISTIC Model Bogdan Gadidov source 8
SGF 2017 Evaluating Predictive Accuracy of Survival Models with PROC PHREG Changbin Guo,
Ying C. So,
Woosung Jang		 source 16
SGF 2017 Fitting a Cumulative Logistic Regression Shana Kelly source 5
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 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 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 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 Is My Model the Best? Methods for Exploring Model Fit Deanna Naomi Schreiber-Gregory 7
MWSUG 2016 To be two or not be two, that is a LOGISTIC question Robert G. Downer 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 Demystifying the CONTRAST and ESTIMATE Statement Hector Lemus source 13
WUSS 2016 Selection Bias: How Can Propensity Score Utilization Help Control For It? Deanna Naomi Schreiber-Gregory source 13
PharmaSUG 2016 ROC Curve: Making way for correct diagnosis Manoj Pandey,
Abhinav Jain		 source 12
PharmaSUG 2016 Unequalslopes: Making life easier when the proportional odds assumption fails Matthew J. Wiedel source 11
SGF 2016 Analyzing Non-Normal Binomial and Categorical Response Variables under Varying Data Conditions Niloofar Ramezani source 16
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 Customer Lifetime Value Modeling Vadim Pliner source
presentation		 4
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 Finding and Evaluating Multiple Candidate Models for Logistic Regression Bruce S. Lund source
zip		 17
SGF 2016 Missing Values: They Are NOT Nothing Justin Jia,
Shan Shan Lin		 source 19
SGF 2016 Probability Density for Repeated Events Bruce S. Lund source 16
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 Fitting and Evaluating Logistic Regression Models Bruce S. Lund 26
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
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 Probability Density for Repeated Events Bruce S. Lund source 16
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
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 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 Identifying Factors Associated with High-Cost Patients Jeff Dang source
zip		 12
SGF 2015 Multinomial Logistic Model for Long-Term Value Bruce S. Lund source 16
SGF 2015 Multiple Ways to Detect Differential Item Functioning in SAS Yan Zhang 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 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 Selection and Transformation of Continuous Predictors for Logistic Regression Bruce S. Lund 13
SESUG 2014 Don't be binary! Tales of Non-binary Categorical Regression Charlotte Baker source 6
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
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
WUSS 2014 Fuzzy Bootstrap Matching Dale Harrington source 9
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 Methodology for Non-Randomized Clinical Trials: Propensity Score Analysis Dan Conroy source 5
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 SAS Macro for Complex Sample Data Analysis Using Generalized Linear Models Paulo Henrique Dourado da Silva,
Alan Ricardo da Silva		 source 19
SGF 2014 Examples of Logistic Modeling with the SURVEYLOGISTIC Procedure Rob Agnelli source 17
SGF 2014 Leveraging Publicly Available Data in the Classroom Using SAS PROC SURVEYLOGISTIC Tyler C. Smith,
Besa Smith		 source 18
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 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 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
SCSUG 2013 Preparing Interaction Variables for Logistic Regression Bruce S. Lund 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
PHUSE EU 2013 Overview of Receiver Operating Characteristic (ROC) Curves in SAS PROC LOGISTIC Paul Constantin source
presentation source		 1
MWSUG 2013 A Tutorial on PROC LOGISTIC Arthur X. Li 19
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 Managing and Monitoring Statistical Models Nathaniel Derby 13
NESUG 2013 Proc SurveyCorr Jessica R. Hampton 7
PharmaSUG 2013 Combining Analysis Results from Multiply Imputed Categorical Data Bohdana Ratitch,
Ilya Lipkovich,
Michael O'Kelly		 source 19
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 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 The Value of Neighborhood Information in Prospect Selection Models: Investigating the Optimal Level of Granularity Dirk Van den Poel,
Philippe Baecke		 source 15
NESUG 2012 Efficiently Screening Predictor Variables for Logistic Models Steven Raimi,
Bruce S. Lund		 13
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 ODS Output Data Sets That Work for You Stuart D. Long,
Edward Heaton,
Daniel R. Scharf		 source 29
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 Use of SAS to determine broiler chick feed color preference in correlation with performance under different housing light colors Nichole Graham 4
WUSS 2012 Up To Speed With Categorical Data Analysis Maura E. Stokes source 25
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
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 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 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
SESUG 2011 Analysis of a Binary Outcome Variable Using the FREQ and LOGISTIC Procedures Arthur X. Li source 18
SESUG 2011 Tailoring Logistic Regression Model Analyses with the ODDSRATIO Statement in PROC LOGISTIC Taylor Lewis source 8
WUSS 2011 Up To Speed With Categorical Data Analysis Maura E. Stokes source 25
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 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 Implementing a Customer Lifetime Value Framework in SAS Krishna Mehta 8
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 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 Logit Models in Practice: B, C, E, G, M, N, O, ... Joseph C. Gardiner,
Zhehui Luo		 source 20
SGF 2011 ODS Output Data Sets That Work for You Stuart D. Long,
Edward Heaton,
Daniel R. Scharf		 source 29
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
SCSUG 2010 Boost Your Confidence (Intervals) With SAS Peter Langlois source 42
WUSS 2010 Dummy Coding for Dummies Kathryn Martin source 10
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 SAS Macros for Estimating the Attributable Benefit of an Optimal Treatment Regime Jason Brinkley 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
SESUG 2009 A New Effect Modification P Value Test Demonstrated Manojkumar B Agravat source 14
SESUG 2009 Using ODS Select to Limit SAS Output Matt Reich source 4
MWSUG 2009 Demonstration of Organic Growth Modeling for B to B Marketing Karen Ziton 15
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 Ranking Predictors in Logistic Regression Douglas Thompson 13
NESUG 2009 What Drives Physicians to Use A Particular Product when Prescribing Marc States 5
WUSS 2009 Using PROC LOGISTIC to Estimate Willingness to Pay for Fresh Produce Mikael Peterson source 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 Beyond SAS/Genetics Amina Barhdadi,
Yassamin Feroz Zada,
Marie-Pierre Dube		 source 10
SGF 2009 Image is Almost Everything: Displaying Statistics Via ODS R. Scott Leslie source 8
WUSS 2008 Demystifying Popular SAS Procedures. Lisa Smith source 17
WUSS 2008 Using Propensity Scores To Adjust For Treatment Selection Bias R. Scott Leslie source 7
PHUSE EU 2008 A Collection of SAS Macros to Calculate Odds Ratios using Spline Regression Martin Gregory source 9
MWSUG 2008 Long-Term Value Modeling in the Automobile Industry Jeff Ames,
Cathy Hackett,
Bruce S. Lund		 6
NESUG 2008 Breakthrough Improvements through Reliable Modern Testing using SAS and JMP Sunil Kumar Gupta 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 Automated SAS Macro for Adverse Event Logistic Regression Analysis Suhas R. Sanjee 8
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
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 Convergence Failures in Logistic Regression Paul D. Allison source 11
SGF 2008 How to Use SAS to Fit Multiple Logistic Regression Models Anpalaki J. Ragavan source 13
SGF 2008 Old Versus New: A Comparison of PROC LOGISTIC and PROC GLIMMIX Rebecca Christofferson source 6
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 Using the SAS DATA Step and PROC SQL to Create Macro Arrays Stuart D. Long,
Edward Heaton		 source 11
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 PROC LOGISTIC: The Logistics Behind Interpreting Categorical Variable Effects Taylor Lewis 7
NESUG 2007 Summarizing the Power of Your Predictive Model with Receiver Operating Characteristic Curves Mithat Gonen 18
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 SAS DATA Step and PROC SQL to Create Macro Arrays Stuart D. Long,
Edward Heaton		 11
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 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 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
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
PharmaSUG 2007 Cochran-Armitage Trend Test Using SAS Hui Liu 11
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
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 Improving Credit Scoring by Generalized Additive Model Wensui Cela,
Jimmy Liu		 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 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
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 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
NESUG 2006 Logistic Regression Basics Joseph J. Guido,
Paul Winters,
Adam Rains		 7
NESUG 2006 Receiver Operating Characteristic (ROC) Curves Mithat Gonen 18
NESUG 2006 Statistical analysis of clustered data using SAS Gui-Shuang Ying,
Chengcheng Liu		 13
PharmaSUG 2006 From GLM to GLIMMIX-Which Model to Choose? Patricia B. Cerrito 7
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 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 Fixed Effects Regression Methods in SAS Paul D. Allison source 20
SUGI 2006 Receiver Operating Characteristic (ROC) Curves Mithat Gonen source 18
SUGI 2006 Using SAS ODS to Extract and Merge Statistics from Multiple SAS Procedures Stuart D. Long,
Jeffrey M. Abolafia,
Lawrence Park		 source 21
SESUG 2005 Using ODS to Perform Simulations on Statistics from SAS Procedures Andrew A. Kramer source 4
WUSS 2005 A Peek at PROC DOCUMENT Andrew H. Karp source 5
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
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 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 Statistical Methods in Diagnostic Medicine Using SAS Software Jayawant N. Mandrekar,
Sumithra J. Mandrekar		 source 8
SUGI 2005 Using Procedure-Based ODS Data Components in Statistical Reporting Vincent J. Faber 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 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 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
MWSUG 2004 Analysis of Complex Sample Survey Data Patricia A. Berglund 7
MWSUG 2004 Presenting the Binary Logit Model Effectively using SAS Hun Myoung Park 11
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 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 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 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
SESUG 2003 Advantages of PROC SCORE Mark Tabladillo source 2
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 The Topic Scoring Engine [presentation] Ulrich Reincke source 37
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 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 Using SAS Software and Visual Basic for Applications to Produce Microsoft Graph Charts Lori S. Parsons source 10
SCSUG 2002 Statistical Pattere Recognition Using Enterprise Miner and Self-Organizing Maps Keith A. Jones 8
NESUG 2002 Two Useful Macros to Nudge SAS to Serve You David Izrael,
Michael P. Battaglia		 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
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 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 How to Use SAS for Logistic Regression with Correlated Data Oliver Kuss source 5
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 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
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
VIEWS 2001 The Version 8 Applied Statistician David Shannon 15
SESUG 2001 GETTING STARTED WITH PROC LOGISTIC Andrew H. Karp source 5
SCSUG 2001 Getting Started with PROC LOGISTIC Andrew H. Karp 5
PharmaSUG 2001 Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques Lori S. Parsons 6
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 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 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
SESUG 2000 Performing Exact Logistic Regression with the SAS R /AD System Robert E. Derr source 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 NPV Modeling for Direct Mail Insurance C. Olivia Parr Rud 7
NESUG 2000 The Identification of Dental Underutilization Patterns for the Mentally Retarded: Univariate and Multivariate Approaches Guy Pascale 3
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
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 SAS Software to Perform a Case-Control Match on Propensity Score in an Observational Study Lori S. Parsons source 6
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
WUSS 1999 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud source 6
WUSS 1999 Modeling Individual Utility Functions Using SAS PROC LOGISTIC: A Case Study Using the NES Data Set Godofredo Gatus source 4
NESUG 1999 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud 7
NESUG 1999 The missing link: PROC GENMOD Margaret Ann Goetz 3
MWSUG 1999 Using Logistic Regression to Predict Customer Retention Andrew H. Karp 5
SUGI 1999 How to Display Correlated ROC Curves with the SAS System Barbara G. Schneider source 5
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
NESUG 1998 Data Mining: An Overview of Methods and Techniques for Increasing Profits in Direct Marketing C. Olivia Parr Rud 8
NESUG 1998 Using Logistic Regression to Predict Customer Retention Andrew H. Karp 5
SESUG 1998 Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Parr Rud 7
PNWSUG 1998 Stepwise Anything: Reading in Regression Procedure Output for Further Testing Ted Clay,
Linda Remy		 10
SEUGI 1998 Displaying and comparing correlated ROC curves with the SAS software Barbara G. Schneider source 8
SUGI 1998 An Information-Gain Measure of Fit in PROC LOGISTIC Ernest S. Shtatland,
Mary B. Barton		 source 6
SUGI 1998 Reporting Results of Multiple Logistic Regression Models Depending on the A vailability of Data Richard M. Mitchell source 5
NESUG 1997 Information as a Unifying Measure of Fit in SAS Statistical Modeling Procedures Ernest S. Shtatland 7
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
SESUG 1997 Modeling Higher Education Processes with Multiple Logistic Regression: SAS Examples Daniel Teodorescu 5
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 Getting Started with PROC LOGISTIC Andrew H. Karp source 5
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 Odds Ratios in a Tabular Presentation Stephen M. Noga,
Dingyi Zhao		 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
NESUG 1996 Between a ROC Curve and a Hard Place Joseph Kufera,
Kimberly A. Mitchell		 10
WUSS 1996 Logistic Regression: From SAS Coding to Statistical Interpretation Elizabeth Li source 9
SUGI 1996 New Age Marketing: Past Life Regression vs. LogisticRegression C. Olivia Parr Rud source 5
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 Plots for Influential and Poorly Fitting Covariate Patterns in the LOGISTIC Procedure Mark Moss,
William C. Murphy		 source 6
NESUG 1994 PROC LOGISTIC: A Form of Regression Analysis Edith Flaster 3
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 Practical Aspects of Modelling Techniques in Logistic Regression Procedures of the SAS System Josef Hogel source 6
SUGI 1994 SAS 6.09 on a CONVEX C3840: First Impressions Kenneth A. Hardy,
Sally M. Muller		 source 8
WUSS 1993 A Macro for Personalized Odds Ratios Alice Pressman,
K. L. Stone,
Estie Hudes,
J. L. Tao		 source 4
WUSS 1993 The LACKFIT Option in SAS LOGISTIC Procedure: A Problem and a Proposed Solution Estie Hudes,
L. Palermo		 source 2
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
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 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 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
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
SEUGI 1990 Directions of the SAS System in Statistics and Decision Support Gerhard Held source 7
SUGI 1985 Application of the SAS System to Regression Analysis of Tumor Prevalence Data Saeid B. Amini,
Debra L. Palka		 source 7

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