Subtopic Deep Dive
SAS Methods for Categorical Data Analysis
Research Guide
What is SAS Methods for Categorical Data Analysis?
SAS methods for categorical data analysis use PROC GENMOD and PROC LOGISTIC to perform logistic regression, log-linear models, stratified analyses, GEE for correlated binary outcomes, and exact tests for small samples.
These methods enable analysis of binary, ordinal, and nominal data in SAS software. Key procedures include PROC GENMOD for generalized linear models and PROC LOGISTIC for binary outcomes (Spiegelman, 2005; 1735 citations). Over 10 papers document macros and streamlined approaches for risk ratios and prevalence differences.
Why It Matters
SAS categorical methods produce valid inferences from contingency tables, prevalence ratios, and risk differences in biomedical studies. Spiegelman (2005) provides direct SAS code for risk or prevalence ratios, cited 1735 times in epidemiology. Liu et al. (2019) macros automate routine analyses for observational data, enabling reproducible reports in clinical research (73 citations). Muthusi et al. (2019) %svy_logistic_regression macro generates publication-ready tables for survey logistic models (7 citations).
Key Research Challenges
Confounding Assessment
Change-in-estimate strategy requires manual iteration across covariates in PROC LOGISTIC or GENMOD. Atashili and Ta (2007) developed a SAS macro to automate this, addressing limitations in standard procedures. Manual methods increase error risk in observational epidemiology.
Model Selection Limitations
PROC GENMOD lacks model selection options available in PROC LOGISTIC or REG. Su (2007) uses macros and ODS to extend capabilities across procedures. This gap complicates generalized linear model comparisons for categorical data.
Survey Data Logistic Regression
Standard PROC LOGISTIC ignores complex survey designs, biasing estimates. Muthusi et al. (2019) created %svy_logistic_regression macro for survey and non-survey data with publication tables. Weighted analyses demand specialized macros for valid inference.
Essential Papers
Easy SAS Calculations for Risk or Prevalence Ratios and Differences
Donna Spiegelman · 2005 · American Journal of Epidemiology · 1.7K citations
Easy SAS Calculations for Risk or Prevalence Ratios and DifferencesWe would like to make the readership aware that risk or prevalence ratios and differences, when they are the parameter of interest...
Carrying out streamlined routine data analyses with reports for observational studies: introduction to a series of generic SAS® macros
Yuan Liu, Dana Nickleach, Chao Zhang et al. · 2019 · F1000Research · 73 citations
<ns4:p>For a typical medical research project based on observational data, sequential routine analyses are often essential to comprehend the data on hand and to draw valid conclusions. However, gen...
Software for Multilevel Analysis
Jan de Leeuw, Ita G. G. Kreft · 2011 · eScholarship (California Digital Library) · 33 citations
In this paper we review some of the more important software programs and packages that can are designed for, or can be used for, multilevel analysis. These programs differ in many respects. Some ar...
Statistical Analysis of Medical Data Using SAS
Geoff Der · 2006 · Biometrics · 24 citations
Abstracts not available for BookReviews
Logistic Regression Basics
Joseph J. Guido, Paul Winters, Adam B Rains · 2006 · 16 citations
What is regression? What’s the difference between linear and logistic regression? When and how should I use them? While these are common questions when students first encounter modeling procedures,...
%svy_logistic_regression: A generic SAS macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data
Jacques Muthusi, Samuel Mwalili, Peter W. Young · 2019 · PLoS ONE · 7 citations
The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use...
Evaluating Predictive Models: Computing and Interpreting the c Statistic
Sigurd Hermansen · 2008 · 7 citations
Automation of predictive model selection has become the alchemy of today’s Business Intelligence (BI), with BI practitioners hoping to transform jargon and acronyms into gold. While statistical mod...
Reading Guide
Foundational Papers
Start with Spiegelman (2005; 1735 citations) for core PROC GENMOD risk ratio code, then Guido et al. (2006; 16 citations) for logistic basics, followed by Der (2006) for medical data applications.
Recent Advances
Study Liu et al. (2019; 73 citations) for streamlined macros and Muthusi et al. (2019; 7 citations) for survey logistic regression tables.
Core Methods
Core techniques: PROC LOGISTIC for binary outcomes, PROC GENMOD for Poisson/log-binomial models and GEE, macros for automation (%svy_logistic_regression, %RR), ODS for custom output.
How PapersFlow Helps You Research SAS Methods for Categorical Data Analysis
Discover & Search
Research Agent uses searchPapers to find Spiegelman (2005) on risk ratios, then citationGraph reveals 1735 citing papers on SAS categorical methods. findSimilarPapers identifies Liu et al. (2019) macros from query 'SAS macros categorical analysis'. exaSearch surfaces macros like %svy_logistic_regression for survey logistic regression.
Analyze & Verify
Analysis Agent runs readPaperContent on Spiegelman (2005) to extract PROC GENMOD code for prevalence ratios. verifyResponse (CoVe) checks logistic regression syntax against Muthusi et al. (2019). runPythonAnalysis recreates risk ratio calculations with pandas for GRADE B evidence verification on small-sample exact tests.
Synthesize & Write
Synthesis Agent detects gaps in GEE implementations for correlated outcomes, flagging contradictions between Spiegelman (2005) and de Leeuw (2011). Writing Agent uses latexEditText to format SAS code, latexSyncCitations for 10+ macro papers, and latexCompile for publication-ready methods sections. exportMermaid visualizes PROC LOGISTIC workflow diagrams.
Use Cases
"Replicate Spiegelman risk ratio SAS code and verify with Python"
Research Agent → searchPapers 'Spiegelman 2005' → Analysis Agent → readPaperContent + runPythonAnalysis (pandas replicate ratios) → statistical verification output with p-values and 95% CIs.
"Generate LaTeX table from Muthusi svy_logistic_regression macro results"
Research Agent → findSimilarPapers '%svy_logistic_regression' → Writing Agent → latexEditText (table formatting) → latexSyncCitations (add Muthusi 2019) → latexCompile → PDF with publication-ready survey logistic table.
"Find GitHub repos implementing SAS GENMOD macros for categorical data"
Research Agent → searchPapers 'SAS GENMOD macros' → Code Discovery workflow (paperExtractUrls → paperFindGithubRepo → githubRepoInspect) → verified SAS macro code and execution examples.
Automated Workflows
Deep Research workflow scans 50+ SAS papers via searchPapers → citationGraph on Spiegelman (2005) → structured report ranking macros by citations for categorical analysis. DeepScan applies 7-step verification: readPaperContent on Liu et al. (2019) → runPythonAnalysis replicate → CoVe checkpoints → GRADE-graded evidence summary. Theorizer generates GEE extensions from de Leeuw (2011) multilevel SAS review.
Frequently Asked Questions
What defines SAS methods for categorical data analysis?
PROC GENMOD handles generalized linear models including log-linear and GEE; PROC LOGISTIC fits binary logistic regression with stratified options (Spiegelman, 2005).
What are common methods in this subtopic?
Macros automate risk ratios (%RR), survey logistic (%svy_logistic_regression), and confounding assessment (Atashili 2007). PROC GENMOD supports exact tests for small samples.
What are key papers?
Spiegelman (2005; 1735 citations) for risk/prevalence ratios; Liu et al. (2019; 73 citations) for routine macro analyses; Muthusi et al. (2019; 7 citations) for survey logistic tables.
What open problems exist?
Limited model selection in PROC GENMOD (Su, 2007); automation gaps for multilevel categorical data (de Leeuw, 2011); scalability of exact tests for large contingency tables.
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