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least squares mean

63 papers and 2 presentations; 61 titles.

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Conference × Year × Title × Authors Extras Pages
PharmaSUG CN 2026 Handling Missing Data in Analgesia Clinical Trials and Corresponding SAS Implementation Jingbo Zhu,
Jiehan Zhang		 source 17
PharmaSUG 2026 A Standardized R Graph Library for Production-Ready Analysis Figures Chunting Zheng,
Margaret Huang,
Xindai Hu		 source 19
PHUSE US 2026 Multiple Sclerosis Trials Unpacked: Standards, Endpoints and Programming Experience Daryna Teteriuk source
presentation source		 12
PharmaSUG CN 2024 Application of I&I Late Phase in Programming Renxiang Wang source 15
PharmaSUG CN 2024 Implementation on concentration-QTc modeling Daoqing Li,
Deli Cao		 source 20
PharmaSUG CN 2024 LSMEANS vs ESTIMATE in proc mixed Gloria Zhong,
Sukie Gao,
Wayne Yang		 source 12
PharmaSUG 2023 Guidelines for the Statistical Analysis in German Dossier Submissions Meiling Gao,
Xi Qian,
Hui Mao		 source 8
PHUSE US 2023 Display Layout Specifications to Flexibly Design and Generate Tables [presentation] Songgu Xie,
Michael Pannucci		 source
video		 29
PHUSE EU 2021 Bringing to Light - Analysis Results Metadata v1.0 for Define-XML v2 [presentation] Hrishikesh K S,
Limna Salim		 source
video		 11
SGF 2021 Bayesian Analysis of GLMMs Using PROC BGLIMM Walter W. Stroup source
pdf		 25
PHUSE EU 2020 MI FOR MI, OR HOW TO HANDLE MISSING INFORMATION WITH MULTIPLE IMPUTATION Daria Shamaida source
video
presentation source		 8
SESUG 2019 ePRO: A View from Statistical Programmer Vijetha Kode,
Saigovind Chenna		 source 18
SESUG 2019 Regression Models for Count Data Jason Brinkley source 8
PharmaSUG 2019 An Innovative Efficacy Table Programming to Automate Its Figure Generation to Ensure Both High Quality and Efficiency Xiangchen (Bob) Cui,
Letan (Cleo) Lin		 source 23
SESUG 2018 Regression Models for Count Data Jason Brinkley source 8
PharmaSUG 2018 Manipulating Statistical and Other Procedure Output to Get the Results That You Need Vincent DelGobbo source 37
SGF 2018 Manipulating Statistical and Other Procedure Output to Get the Results That You Need Vincent DelGobbo source 37
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 Visualizing Multiple Endpoints: Extending the Use of Forest Plots in Clinical Trials James R. Johnson,
Mark Jaros		 source
presentation source		 10
WUSS 2016 How D-I-D you do that? Basic Difference-in-Differences Models in SAS Margaret Warton,
Melissa Parker,
Andrew Karter		 source 11
PharmaSUG 2016 Mixed Effects Models Yan Wang source 7
SGF 2015 Using and Understanding the LSMEANS and LSMESTIMATE Statements David J. Pasta source 20
SESUG 2014 Making Comparisons Fair: How LS-Means Unify the Analysis of Linear Models Weijie Cai source 22
PHUSE EU 2014 Multiple Imputation: Better than Single Imputation in Pain Studies Ashik Chowdhury source
presentation source		 12
WUSS 2014 Using and Understanding LSMEANS and LSMESTIMATE David J. Pasta source 20
PharmaSUG 2014 Quickly Organize Statistical Output for Review Bradford J. Danner source 4
SGF 2014 Making Comparisons Fair: How LS-Means Unify the Analysis of Linear Models Weijie Cai source 22
SGF 2013 Adding New Rows in the ADaM Basic Data Structure: When and How Mario Widel,
Sandra Minjoe		 source 12
SGF 2013 Being Continuously Discrete (or Discretely Continuous): Understanding Models with Continuous and Discrete Predictors and Testing Associated Hypotheses David J. Pasta source 19
WUSS 2012 Being Continuously Discrete (or Discretely Continuous): Understanding Models with Continuous and Discrete Predictors and Testing Associated Hypotheses David J. Pasta source 19
PharmaSUG 2012 Application of Meta-Analysis in Clinical Trials Marina Komaroff source 7
PharmaSUG 2011 From SAP to BDS: The Nuts and Bolts Nancy Brucken,
Paul K. Slagle		 source 9
SGF 2011 Those Confounded Interactions: Building and Interpreting a Model with Many Potential Confounders and Interactions David J. Pasta source 13
WUSS 2010 Those Confounded Interactions: Building and Interpreting a Model with Many Potential Confounders and Interactions David J. Pasta source 9
PHUSE EU 2010 Useful Tips for Analysis of Variance (ANOVA) in Multicentre, Placebo Controlled Clinical Trials Joanna Romaniuk source
presentation source		 12
MWSUG 2010 Comparing JMP and SAS for Validating Clinical Trials Sandra D. Schlotzhauer 18
MWSUG 2010 Using HPMIXED and Other SAS Procedures to Efficiently Analyze Large Dimension Registry Data Matthew Fenchel,
Gary McPhail,
Rhonda D. VanDyke		 19
PharmaSUG 2010 SAS Programmer's check list-Quick checks to be done before the statistical reports go off the SAS Programmer's table Thomas T. Joseph 7
SGF 2010 Introducing PROC PLM and Postfitting Analyses in Very General Linear Models Randy Tobias,
Weijie Cai		 source 18
SGF 2010 Practicalities of Using ESTIMATE and CONTRAST Statements David J. Pasta source 20
SGF 2009 Comparison of Features and Applications of Four Linear Models Procedures Larry W. Douglass source 19
PHUSE EU 2006 Proc mixed -- Trials and Tribulations Shelley Fordred source 7
SUGI 2006 AMMI Macros for Multiplicative Interaction Models Eun-Joo Lee,
Dallas E. Johnson		 source 10
NESUG 2005 Adopting New Techniques: Statistical Procedures to Determine the Agreement between two Laboratory Methods Ashley Peterson,
Larry W. Douglass		 13
PharmaSUG 2004 "Dualing" Arrays: Reducing DATA Step Passes with Array Look-Ups Nancy Brucken,
Lisa R. Reeves		 7
PharmaSUG 2004 Automate Pharmacodynamic Analyses of Repeated Measure in Clinical Trials Using SAS Mixed Procedure Adeline Yeo,
Grace Li		 5
PharmaSUG 2004 Generating Least Square Means, Standard Error, Observed Mean, Standard Deviation and Confidence Intervals for Treatment Differences using Proc Mixed Richann J. Watson 12
SESUG 2002 Using ODS to Capture Statistics from PROC GLM Frank L. Roediger source 6
PharmaSUG 2000 Mixed Models Analysis Using JMP Software 4.0 George A. Milliken 6
SUGI 2000 Mixed-Up Mixed Models: Things That Look Like They Should Work But Don't, and Things That Look Like They Shouldn't Work But Do Robert M. Hamer,
Pippa M. Simpson		 source 8
SUGI 2000 Modeling Longitudinal Growth Data and Growth Percentiles with Polynomial Gompertz Model in SAS Software Inna Perevozskaya,
Olga M. Kuznetsova		 source 6
MWSUG 1999 Sample Size Determination From A Pilot Bioequivalence Study For A Future Pivotal Bioequivalence Study: A SAS Procedure Xiang Gao,
Henry Cheng		 7
NESUG 1997 Estimation of Direct, Period and Carryover Effects in Crossover Studies Mitchell Kotler 7
NESUG 1996 BIOEQ4: Bioequivalence Macro to Create both Table and SAS data set according to the FDA Bioequivalence Guidelines Issued in 1992 Mary Kay McPherson 6
MWSUG 1996 A Set of SAS Macros for Producing Statistical Reports Greg A. Grandits 10
SUGI 1993 Strategies for Performing Multiple Comparisons on Means Jenny Aquino Kendall source 7
SUGI 1992 Generating Statistical Tables Meeting NDA Guidelines the Lazy Way John Lue source 6
SUGI 1990 Analyzing Balanced or Unbalanced Latin Squares and Other Repeated-Measures Designs for Carryover Effects Using the GLM Procedure D. A. Ratkowsky,
J. R. Alldredge,
J. W. Cotton		 source 6
SUGI 1990 JMP Tutoria! John P. Sall source 8
SUGI 1989 A General Approach in the Analysis of Repeated Measures Data Using the GLM Procedure Lee Kaiser source 6
SUGI 1984 PROC GLM-Rearrangement of Output Karen L. Bourn source 6
SUGI 1984 Using SAS Software to Produce Summary Tables of GLM-Calculated Statistics Margaret A. Grady source 6
SEUGI 1983 GLMOUT - A SAS Program to Read PROC GLM Output A. Eastwood,
D. A. Budgett		 7
SUGI 1981 Estimation of Fixed Effects in Mixed Models with Unbalanced Data Walter R. Harvey source 7
SUGI 1980 Least Squares Means in the Fixed Effect Analysis of Variance Kenneth L. Koonce,
F. Michael Speed		 source

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