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Physical Sciences · Engineering

SAS software applications and methods
Research Guide

What is SAS software applications and methods?

SAS software applications and methods refer to the use of SAS programming language and procedures for statistical analysis, data modeling, and computation in research, as detailed in specialized textbooks and guides.

The field encompasses 32,664 works on SAS applications, including mixed models, categorical data analysis, and repeated measures data handling. Key resources cover SAS commands for statistics, such as those in "Discovering Statistics Using SPSS" by Andy P. Field and Jeremy N. V. Miles (2000), adapted for SAS with updated programming. Growth over the past 5 years is not available in the data.

Topic Hierarchy

100%
graph TD D["Physical Sciences"] F["Engineering"] S["Biomedical Engineering"] T["SAS software applications and methods"] D --> F F --> S S --> T style T fill:#DC5238,stroke:#c4452e,stroke-width:2px
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32.7K
Papers
N/A
5yr Growth
72.2K
Total Citations

Research Sub-Topics

SAS Programming for Linear Mixed Models

This sub-topic covers PROC MIXED applications for hierarchical data, random effects modeling, and repeated measures analysis. Researchers develop macros for model diagnostics, covariance structures, and power calculations.

15 papers

SAS Methods for Categorical Data Analysis

This sub-topic details PROC GENMOD and PROC LOGISTIC for logistic regression, log-linear models, and stratified analyses. Researchers implement GEE for correlated binary outcomes and exact tests for small samples.

15 papers

Survival Analysis Using SAS

This sub-topic employs PROC PHREG for Cox models, PROC LIFETEST for Kaplan-Meier curves, and competing risks with PROC PHCREG. Researchers handle time-dependent covariates, frailty models, and landmark analyses.

SAS Macro Programming Techniques

This sub-topic teaches %macro/%mend constructs, conditional logic, looping, and SQL integration for automating analyses. Researchers share reusable macros for data cleaning, simulation, and reporting.

15 papers

Advanced SAS Graphics and Visualization

This sub-topic utilizes PROC SGPLOT, SGSCATTER, and ODS for publication-quality plots, heatmaps, and interactive dashboards. Researchers customize forest plots, ROC curves, and spaghetti plots for complex results.

Why It Matters

SAS software enables precise calculations for risk or prevalence ratios in epidemiology, as shown in "Easy SAS Calculations for Risk or Prevalence Ratios and Differences" by Donna Spiegelman (2005), where direct SAS code computes these parameters avoiding complex alternatives. In repeated measures analysis, "Modelling covariance structure in the analysis of repeated measures data" by Ramon C. Littell, Jane Pendergast, and Ranjini Natarajan (2000) applies SAS to model correlations in longitudinal data from medical studies. "SAS System for Mixed Models" by Ramon C. Littell (1996) supports hierarchical modeling in biomedical engineering, facilitating analysis of clustered data like patient outcomes across trials.

Reading Guide

Where to Start

"Discovering Statistics Using SPSS" by Andy P. Field and Jeremy N. V. Miles (2000), because it provides an accessible introduction to SAS commands and programming with practical examples for students new to statistical software.

Key Papers Explained

"Discovering Statistics Using SPSS" by Field and Miles (2000) introduces core SAS syntax, building foundational skills applied in "SAS System for Mixed Models" by Littell (1996) for advanced hierarchical modeling. "Categorical Data Analysis Using the SAS System" (1996) extends these basics to logistic regression and tables, while "Modelling covariance structure in the analysis of repeated measures data" by Littell, Pendergast, and Natarajan (2000) refines mixed models from Littell (1996) with covariance specification. "SAS System for Linear Models" by Marasinghe et al. (1988) connects linear foundations to these extensions.

Paper Timeline

100%
graph LR P0["The discrete correlation functio...
1988 · 1.2K cites"] P1["SAS System for Linear Models
1988 · 1.1K cites"] P2["SAS System for Mixed Models
1996 · 10.4K cites"] P3["Categorical Data Analysis Using ...
1996 · 1.9K cites"] P4["Discovering Statistics Using SPSS
2000 · 27.8K cites"] P5["Modelling covariance structure i...
2000 · 926 cites"] P6["Easy SAS Calculations for Risk o...
2005 · 1.7K cites"] P0 --> P1 P1 --> P2 P2 --> P3 P3 --> P4 P4 --> P5 P5 --> P6 style P4 fill:#DC5238,stroke:#c4452e,stroke-width:2px
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Most-cited paper highlighted in red. Papers ordered chronologically.

Advanced Directions

Current work builds on covariance modeling from Littell et al. (2000) and mixed models in Littell (1996), with no recent preprints or news available to indicate shifts.

Papers at a Glance

# Paper Year Venue Citations Open Access
1 Discovering Statistics Using SPSS 2000 Medical Entomology and... 27.8K ✕
2 SAS System for Mixed Models 1996 Medical Entomology and... 10.4K ✕
3 Categorical Data Analysis Using the SAS System 1996 Technometrics 1.9K ✕
4 Easy SAS Calculations for Risk or Prevalence Ratios and Differ... 2005 American Journal of Ep... 1.7K ✓
5 The discrete correlation function - A new method for analyzing... 1988 The Astrophysical Journal 1.2K ✕
6 SAS System for Linear Models 1988 Technometrics 1.1K ✕
7 Modelling covariance structure in the analysis of repeated mea... 2000 Statistics in Medicine 926 ✕
8 Applied Statistics and the SAS Programming Language 1998 Technometrics 906 ✕
9 SAS user's guide : basics 1986 Medical Entomology and... 819 ✕
10 Wiley Series in Probability and Mathematical Statistics 2011 Wiley series in probab... 806 ✕

Frequently Asked Questions

What is the role of SAS in mixed models analysis?

"SAS System for Mixed Models" by Ramon C. Littell (1996) provides procedures for fitting mixed-effects models to hierarchical data. These models account for both fixed and random effects in experimental designs. The book details SAS syntax for implementation in statistical research.

How does SAS handle categorical data analysis?

"Categorical Data Analysis Using the SAS System" (1996) covers methods from 2x2 tables to logistic regression using SAS procedures. It includes chapters on sets of tables and nonparametric methods. SAS tools compute odds ratios and fit models directly from contingency data.

What SAS methods exist for repeated measures data?

"Modelling covariance structure in the analysis of repeated measures data" by Ramon C. Littell, Jane Pendergast, and Ranjini Natarajan (2000) uses SAS to specify covariance structures like compound symmetry or autoregressive. This approach analyzes correlations in time-series or spatial data. SAS procedures like PROC MIXED implement these models for valid inference.

How can SAS calculate risk ratios?

"Easy SAS Calculations for Risk or Prevalence Ratios and Differences" by Donna Spiegelman (2005) presents SAS code for direct computation of risk ratios from binary data. This avoids log-binomial regression issues. The method applies to cohort studies in epidemiology.

What are key SAS resources for beginners?

"Discovering Statistics Using SPSS" by Andy P. Field and Jeremy N. V. Miles (2000) adapts content for SAS, teaching commands and programming for introductory statistics. It includes examples for data description and hypothesis testing. The book serves students transitioning to SAS software.

How does SAS support linear models?

"SAS System for Linear Models" by Mervyn G. Marasinghe, Rudolf J. Freund, Ramon C. Littell, and P. Spector (1988) outlines procedures for ANOVA and regression. It covers general linear models with SAS syntax. Applications include experimental design in engineering.

Open Research Questions

  • ? How can SAS procedures be optimized for large-scale repeated measures data with complex covariance structures beyond those in Littell et al. (2000)?
  • ? What extensions of SAS mixed models address unbalanced designs in biomedical time-series not fully covered in Littell (1996)?
  • ? How do SAS methods for categorical data integrate with modern machine learning workflows for high-dimensional epidemiology data?
  • ? What improvements to SAS risk ratio calculations handle zero-cell problems more robustly than Spiegelman (2005)?
  • ? How can SAS programming adapt discrete correlation functions from Edelson and Krolik (1988) for unevenly sampled engineering sensor data?

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